Analysis of the Broad’s Avana CRISPR (Broad Institute Cancer Dependency Map 2018, Meyers et al. 2017) and the Broad and Dana-Farber Cancer Institite’s Achilles shRNA (MacFarland et al. 2018, Data Science 2018).

The Avana screen produced results using CERES (Meyers et al. 2017) (GitHub), which generates gene dependency scores from sgRNA depletion scores from gene essentiality screens and eliminates bias arising from the effect of copy number variation on Cas9 DNA cleavage. The lower the CERES score, the higher the likelihood that the gene is essential in the associated cell line. Scores are scaled per cell line such that a score of 0 is the median effect of nonessential genes and -1 is the median effect of common core essential genes.

All annotation files (copy number, mutation status, and gene expression) (Consortium and Consortium 2015, Barretina et al. 2012) were downloaded from the DepMap Data Portal.

1 Set up

library(bsselectR)
library(data.table)
library(ggpubr)
library(ggsignif)
library(kableExtra)
library(tidyverse)

2 Functions

2.1 Significance thresholding

adj_signif <- function(df) {
  # Takes df from compare_means and creates a signif code column for adjusted p-vals
  df$p.signif <- ifelse(df$p.signif == "ns", NA, df$p.signif)
  df$p.signif.adj <- ifelse(df$p.adj <= 0.0001, "****",
                     ifelse(df$p.adj <= 0.001, "***",
                     ifelse(df$p.adj <= 0.01, "**",
                     ifelse(df$p.adj <= 0.05, "*", NA))))
  df$p.short <- formatC(df$p, format = "g", digits =  2)
  df$p.adj.short <- formatC(df$p.adj, format = "g", digits =  2)
  
  return(df)
}

2.2 Wilcoxon tests by drug

WilcoxonByDrug <- function(drug, dataset, data_name) {
  use_data <- filter(dataset, Drug == drug)
  sig <- compare_means(AUC ~ Mutation_Status_Nonsilent, group.by = "Hugo_Symbol", data = use_data, method = "wilcox.test", p.adjust.method = "BH")
  sig <- adj_signif(sig)
  sig <- sig[order(sig$p),]
  sig$Drug <- drug
  sig$Dataset <- data_name
  return(sig)
}

2.3 Make per-drug boxplots

makeDrugBoxplots <- function(drug, dataset) {
  if(dataset == "CCLE" | dataset == "ccle") {
    use_data <- filter(ccle_data_g2p_genesfilt, Drug == drug)
    use_color <- as.character(use_data$Color_Nonsilent)
    names(use_color) <- use_data$Mutation_Status_Nonsilent
    sig <- ccle_signif_g2p_ByDrug[[drug]]
    label_text <- data.frame(p.signif = sig$p.signif, p.signif.adj = sig$p.signif.adj, Hugo_Symbol = sig$Hugo_Symbol)
    label_text$Hugo_Symbol <- factor(label_text$Hugo_Symbol, levels = g2p_genes)
  }
  else if(dataset == "CTRP" | dataset == "ctrp") {
    use_data <- filter(ctrp_data_g2p_genesfilt, Drug == drug)
    use_color <- as.character(use_data$Color_Nonsilent)
    names(use_color) <- use_data$Mutation_Status_Nonsilent
    sig <- ctrp_signif_g2p_ByDrug[[drug]]
    label_text <- data.frame(p.signif = sig$p.signif, p.signif.adj = sig$p.signif.adj, Hugo_Symbol = sig$Hugo_Symbol)
    label_text$Hugo_Symbol <- factor(label_text$Hugo_Symbol, levels = g2p_genes)
  }
  else if(dataset == "GDSC" | dataset == "gdsc") {
    use_data <- filter(gdsc_data_g2p_genesfilt, Drug == drug)
    use_color <- as.character(use_data$Color_Nonsilent)
    names(use_color) <- use_data$Mutation_Status_Nonsilent
    sig <- gdsc_signif_g2p_ByDrug[[drug]]
    label_text <- data.frame(p.signif = sig$p.signif, p.signif.adj = sig$p.signif.adj, Hugo_Symbol = sig$Hugo_Symbol)
    label_text$Hugo_Symbol <- factor(label_text$Hugo_Symbol, levels = g2p_genes)
  }
  else { return("Error: Invalid dataset.") }
    
  plot <- ggplot(data = use_data, aes(x = Mutation_Status_Nonsilent, y = AUC)) +
    facet_wrap(~ Hugo_Symbol, drop = FALSE, nrow = 1) +
    geom_boxplot(mapping = aes(fill = Mutation_Status_Nonsilent), position = position_dodge(0.85), outlier.shape = 3, outlier.size = 0.5) +
    scale_fill_manual(values = use_color) +
    guides(color = FALSE) +
    geom_text(data = label_text, mapping = aes(x = 1.5, y = max(use_data$AUC), label = p.signif), nudge_y = 0.2) +
    theme(legend.position = "top", axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank()) +
    labs(fill = "Mutation Status", y = "AUC", x = "Mutation Status", title = paste0(toupper(dataset), ": ", drug))
  return(plot)
}

3 Data management

3.1 D. Charytonowicz cell line converter

This comprehensive cancer cell line information curated by Daniel Charytonowicz.

ccl_converter <- read.delim("./data_munging/cell_line_database_v1_20180911.tsv", row.names = 1, sep = "\t", header = TRUE)

3.2 DepMap cell line metadata

ccl_info <- read.delim("./data_munging/DepMap-2018q3-celllines.csv", sep = ",", header = TRUE, na.strings = c("", NA))
ccl_info$Primary.Disease <- gsub("\\\\", "", ccl_info$Primary.Disease)
ccl_info$Primary.Disease <- gsub("Ewings", "Ewing's", ccl_info$Primary.Disease)

# From figshare
crispr_meta <- read.delim("./data_munging/sample_info_18Q3_crispr.csv", sep = ",", header = TRUE, na.strings = c("", NA))
colnames(crispr_meta)[7] <- "CCLE_Name"

3.3 Genomic annotations

3.3.1 CCLE MAF file

For mutation calling, get paired gene name and cell line fields in a data frame. For this analysis, we don’t care about how many mutations there are per gene or what type of mutations there are, so I didn’t save more information. I took unique gene-cell line combinations since we only cared about mutation presence/absence. Add a Mutation_Status column denoting all entries in MAF files as mutations present in the associated cell lines.

maf_raw <- read.delim("./data_munging/CCLE_DepMap_18q3_maf_20180718.txt.gz", header = TRUE, sep = "\t")
# Filter for cell lines in CRISPR screen
maf_raw <- filter(maf_raw, Broad_ID %in% unique(crispr_meta$Broad_ID))
colnames(maf_raw)[colnames(maf_raw) == "Tumor_Sample_Barcode"] <- "CCLE_Name"

# Select columns
maf_df <- subset(maf_raw, select = c("Hugo_Symbol", "CCLE_Name", "Broad_ID", "Variant_Classification", "Reference_Allele", "Tumor_Seq_Allele1"))
maf_df$Reference_Allele_Length <- ifelse(maf_df$Reference_Allele == "-", 0, nchar(as.character(maf_df$Reference_Allele)))
maf_df$Tumor_Seq_Allele1_Length <- nchar(as.character(maf_df$Tumor_Seq_Allele1))

# Add Mutation_Status column
maf_df$Mutation_Status_Nonsilent <- ifelse(maf_df$Variant_Classification == "Silent", "Other", "Mutant")
maf_df <- unique(subset(maf_df, select = c("Hugo_Symbol", "CCLE_Name", "Broad_ID", "Mutation_Status_Nonsilent", "Reference_Allele_Length", "Tumor_Seq_Allele1_Length")))

3.3.2 CCLE copy number file

cn <- read.delim("./data_munging/public_18Q3_gene_cn_v2.csv.gz", sep = ",", check.names = FALSE, header = TRUE)

# Convert log2 ratios (log2(CN/2)) to CN
cn[2:ncol(cn)] <- lapply(cn[2:ncol(cn)], function(x) 2 * (2 ^ x))

# Remove Entrez gene IDs from colnames
colnames(cn) <- gsub(" .*", "", colnames(cn))
colnames(cn)[1] <- "Broad_ID"

# Melt
cn_melt <- melt(data = cn, id.vars = "Broad_ID", measure.vars = colnames(cn[2:ncol(cn)]), variable.name = "Hugo_Symbol", value.name = "Copy_Number")

saveRDS(cn_melt, "./data_munging/rds/cn_melt_18Q3.rds", compress = "xz")
cn_melt <- readRDS("./data_munging/rds/cn_melt_18Q3.rds")

3.3.3 CCLE gene expression file

Gene expression data (Reads Per Kilobase of transcript, per Million mapped reads, RPKM).

ge <- read.delim("./../crispr_lineages_giant_files/CCLE_DepMap_18q3_RNAseq_RPKM_20180718.gct.gz", skip = 2, header = TRUE, sep = "\t", check.names = FALSE)

# Edit columns
ge$Name <- NULL
colnames(ge)[1] <- "Hugo_Symbol"

# Melt
ge_melt <- melt(data = ge, id.vars = "Hugo_Symbol", measure.vars = colnames(ge[2:ncol(ge)]), value.name = "RPKM")
## Split variable column
ge_melt <- with(ge_melt, cbind(Hugo_Symbol, colsplit(variable, pattern = " ", names = c("CCLE_Name", "Broad_ID")), RPKM))
## Remove parentheses around Broad IDs
ge_melt$Broad_ID <- gsub("\\(|\\)", "", ge_melt$Broad_ID)

saveRDS(ge_melt, "./../crispr_lineages_giant_files/ge_melt_18Q3.rds", compress = "xz")
ge_melt <- readRDS("./../crispr_lineages_giant_files/ge_melt_18Q3.rds")

3.4 G2P

g2p <- read.delim("./data_munging/g2p_full_level_A_dataset_v2_10_10_2018.tsv", sep = "\t", header = TRUE)
g2p$Drug <- tolower(g2p$Drug)
g2p$Drug_Gene <- ifelse(is.na(g2p$Drug) | is.na(g2p$Gene), NA, paste0(g2p$Drug, "_", g2p$Gene))
g2p <- subset(g2p, grepl("^CID", g2p$DrugID))

g2p_drug_genes <- as.character(unique(g2p$Drug_Gene[!is.na(g2p$Drug_Gene)]))
g2p_genes <- unique(sapply(strsplit(g2p_drug_genes, "_", fixed = TRUE), function(x) x[2]))
g2p_drugs <- unique(sapply(strsplit(g2p_drug_genes, "_", fixed = TRUE), function(x) x[1]))

g2p_genes
##  [1] "ALK"     "EGFR"    "PDGFRA"  "JAK2"    "KIT"     "ABL1"    "BRAF"   
##  [8] "BRCA1"   "DPYD"    "G6PD"    "TSC2"    "UGT1A1"  "IDH2"    "PDGFRB" 
## [15] "MET"     "ERBB3"   "KRAS"    "RET"     "ROS1"    "ESR1"    "ERBB2"  
## [22] "BRCA2"   "PML"     "TPMT"    "TSC1"    "MGMT"    "PDGFB"   "AKT1"   
## [29] "MYD88"   "PTCH1"   "DNMT3A"  "CYP19A1" "FLT3"    "NPM1"
g2p_drugs
##  [1] "ceritinib"             "crizotinib"           
##  [3] "gefitinib"             "afatinib"             
##  [5] "imatinib"              "ruxolitinib"          
##  [7] "sunitinib"             "regorafenib"          
##  [9] "dasatinib"             "ponatinib"            
## [11] "brigatinib"            "vemurafenib"          
## [13] "trametinib"            "cobimetinib"          
## [15] "olaparib"              "2-fluoropyrimidine"   
## [17] "erlotinib"             "osimertinib"          
## [19] "dabrafenib"            "everolimus"           
## [21] "nilotinib"             "capecitabine"         
## [23] "enasidenib"            "icotinib"             
## [25] "cabozantinib"          "fluorouracil"         
## [27] "fulvestrant"           "lapatinib"            
## [29] "azd"                   "alectinib"            
## [31] "rucaparib"             "niraparib"            
## [33] "docetaxel"             "tretinoin"            
## [35] "purine"                "guanine"              
## [37] "temozolomide"          "pralidoxime mesylate" 
## [39] "nafomine malate"       "neratinib"            
## [41] "ap26113"               "bosutinib monohydrate"
## [43] "tegafur"               "arsenic"              
## [45] "irinotecan"            "vandetanib"           
## [47] "letrozole"             "ibrutinib"            
## [49] "vismodegib"            "daunorubicin"         
## [51] "pazopanib"             "belinostat"           
## [53] "anastrozole"           "midostaurin"          
## [55] "cyclophosphamide"      "selumetinib"          
## [57] "palbociclib"
# Order and genes taken from pink heatmapo from Daniel's analyses
g2p_genes <- c("KRAS", "EGFR", "MET", "ALK", "BRAF", "ERBB2", "ABL1", "ROS1", "KIT", "DPYD", "PDGFRA", "RET", "BRCA1", "UGT1A1", "IDH2", "ESR1", "TPMT", "BRCA2", "PDGFRB", "G6PD", "PML", "TSC1", "FLT3", "ERBB3", "CYP19A1", "AKT1", "PTCH1", "MGMT", "DNMT3A", "MYD88", "PDGFB", "TSC2", "NPM1", "JAK2")

3.5 CRISPR

3.5.1 DepMap dependency probabilities

dep <- read.delim("./data_munging/gene_dependency_18Q3.csv.gz", sep = ",", header  = TRUE, check.names = FALSE)
# Remove Entrez gene IDs from colnames
colnames(dep) <- gsub(" .*", "", colnames(dep))

3.5.2 Merge CRISPR data and annotations

The latest CRISPR CERES score data (18Q3, August 2018) was pulled from the DepMap Data Portal (Broad Institute Cancer Dependency Map 2018, Meyers et al. 2017).

crispr <- read.delim("./data_munging/gene_effect_18Q3.csv.gz", sep = ",", header  = TRUE, check.names = FALSE)
# Remove Entrez gene IDs from colnames
colnames(crispr) <- gsub(" .*", "", colnames(crispr))

Merge annotation data:

# Melt CRISPR dataset for merging
crispr_melt <- melt(crispr, id.vars = "Broad_ID", measure.vars = colnames(crispr)[2:ncol(crispr)], variable.name = "Hugo_Symbol", value.name = "Score")
# Melt dependency probabilities dataset for merging
dep_melt <- melt(dep, id.vars = "Broad_ID", measure.vars = colnames(dep)[2:ncol(dep)], variable.name = "Hugo_Symbol", value.name = "Dep_Prob")
# Merge dependency probabilities
crispr_melt <- merge(crispr_melt, dep_melt, by = c("Broad_ID", "Hugo_Symbol"), all.x = TRUE)
# Merge cell line metadata
crispr_melt <- merge(crispr_melt, ccl_info, by = "Broad_ID", all.x = TRUE)
crispr_melt <- merge(crispr_melt, crispr_meta, by = c("CCLE_Name", "Broad_ID"), all.x = TRUE)
# Merge mutation annotations
crispr_muts <- merge(crispr_melt, maf_df, by = c("Hugo_Symbol", "CCLE_Name", "Broad_ID"), all.x = TRUE)
crispr_muts$Hugo_Symbol <- factor(crispr_muts$Hugo_Symbol)
crispr_muts <- crispr_muts %>% mutate(Mutation_Status_Nonsilent = if_else(is.na(Mutation_Status_Nonsilent), "Other", Mutation_Status_Nonsilent))
# Summarize number of mutant and Other cell lines
crispr_muts_summ <- crispr_muts %>% group_by(Hugo_Symbol) %>%
  summarize(N_Nonsilent_Other = sum(Mutation_Status_Nonsilent == "Other"),
            N_Nonsilent_Mutant = sum(Mutation_Status_Nonsilent == "Mutant"))
# Merge test results back into full dataset, which restores information lost in the summarization
crispr_data <- merge(crispr_muts_summ, crispr_muts, by = "Hugo_Symbol")
# Add Color columns
crispr_data$Color_Nonsilent <- ifelse(crispr_data$Mutation_Status_Nonsilent == "Other", "thistle", "darkorchid")
crispr_data$Color_Nonsilent <- factor(crispr_data$Color_Nonsilent)
# Cell line lineages
crispr_data <- merge(crispr_data, ccl_converter, by = c("CCLE_Name", "Broad_ID"), all.x = TRUE)
levels(crispr_data$lineage_name) <- sort(levels(crispr_data$lineage_name), decreasing = TRUE)
# Copy number
crispr_data <- merge(crispr_data, cn_melt, by = c("Hugo_Symbol", "Broad_ID"), all.x = TRUE)
# Gene expression (RPKM)
ge_filt <- filter(ge_melt, Hugo_Symbol %in% unique(crispr_data$Hugo_Symbol) & Broad_ID %in% unique(crispr_data$Broad_ID))
crispr_data <- merge(crispr_data, ge_filt, by = c("Hugo_Symbol", "Broad_ID", "CCLE_Name"), all.x = TRUE)
crispr_data$RPKM_log2 <- log2(crispr_data$RPKM + 0.0001)
saveRDS(crispr_data, "./../crispr_lineages_giant_files/crispr_data_18Q3.rds", compress = "xz")
crispr_data <- readRDS("./../crispr_lineages_giant_files/crispr_data_18Q3.rds")

crispr_data_ptmuts <- filter(crispr_data, Reference_Allele_Length == 1 | Reference_Allele_Length == 0 | is.na(Reference_Allele_Length))
crispr_data_ptmuts$Keep_PtMut_Tests <- ifelse(crispr_data_ptmuts$Reference_Allele_Length == 1 | is.na(crispr_data_ptmuts$Reference_Allele_Length), TRUE, ifelse(crispr_data_ptmuts$Tumor_Seq_Allele1_Length == 1, TRUE, FALSE))
crispr_data_ptmuts <- filter(crispr_data_ptmuts, Keep_PtMut_Tests == TRUE)

# Filter CRISPR for G2P genes in G2P
crispr_data_g2p <- filter(crispr_data_ptmuts, Hugo_Symbol %in% g2p_genes)
crispr_data_g2p$Hugo_Symbol <- factor(crispr_data_g2p$Hugo_Symbol, levels = g2p_genes)

3.6 Drug screens

# Drug metadata
drug_meta <- read.delim("./data_munging/drug_id_drug_name_table_10_12_2018.tsv", sep = "\t", header = FALSE)
colnames(drug_meta) <- c("CID", "Drug")
drug_meta$Drug <- tolower(drug_meta$Drug)
maf_df_g2p <- filter(maf_df, Hugo_Symbol %in% g2p_genes)

# CCLE
ccle <- read.delim("./data_munging/data_drug_auc_ccle_10_12_2018.csv", sep = "\t", header  = TRUE, row.names = 1, check.names = FALSE)
ccle <- merge(drug_meta, ccle, by = "CID")
ccle_melt <- melt(ccle, id.vars = c("CID", "Drug"), measure.vars = colnames(ccle)[3:ncol(ccle)], variable.name = "accession_id", value.name = "AUC")
ccle_grid <- expand.grid("accession_id" = unique(ccle_melt$accession_id), "Hugo_Symbol" = g2p_genes)
ccle_data <- merge(ccle_grid, ccle_melt, by = "accession_id", all.x = TRUE)
ccle_data <- merge(ccle_data, ccl_converter, by = "accession_id", all.x = TRUE)
ccle_data <- merge(ccle_data, maf_df_g2p, by = c("Hugo_Symbol", "CCLE_Name", "Broad_ID"), all.x = TRUE)
ccle_data$Mutation_Status_Nonsilent <- ifelse(is.na(ccle_data$Mutation_Status_Nonsilent), "Other", ccle_data$Mutation_Status_Nonsilent)
ccle_data$Hugo_Symbol <- factor(ccle_data$Hugo_Symbol)
ccle_data$AUC <- as.numeric(ccle_data$AUC)
ccle_data$Color_Nonsilent <- ifelse(ccle_data$Mutation_Status_Nonsilent == "Other", "palegreen3", "springgreen4")
ccle_data$Color_Nonsilent <- factor(ccle_data$Color_Nonsilent)
ccle_data <- filter(ccle_data, !is.na(ccle_data$AUC))
ccle_data$Drug_Gene <- paste0(ccle_data$Drug, "_", ccle_data$Hugo_Symbol)
saveRDS(ccle_data, "./data_munging/rds/ccle_data_18Q3.rds", compress = "xz")

# CTRP
ctrp <- read.delim("./data_munging/data_drug_auc_ctrp_10_12_2018.csv", sep = "\t", header  = TRUE, row.names = 1, check.names = FALSE)
ctrp <- merge(drug_meta, ctrp, by = "CID")
ctrp_melt <- melt(ctrp, id.vars = c("CID", "Drug"), measure.vars = colnames(ctrp)[3:ncol(ctrp)], variable.name = "accession_id", value.name = "AUC")
ctrp_grid <- expand.grid("accession_id" = unique(ctrp_melt$accession_id), "Hugo_Symbol" = g2p_genes)
ctrp_data <- merge(ctrp_grid, ctrp_melt, by = "accession_id", all.x = TRUE)
ctrp_data <- merge(ctrp_data, ccl_converter, by = "accession_id", all.x = TRUE)
ctrp_data <- merge(ctrp_data, maf_df_g2p, by = c("Hugo_Symbol", "CCLE_Name", "Broad_ID"), all.x = TRUE)
ctrp_data$Mutation_Status_Nonsilent <- ifelse(is.na(ctrp_data$Mutation_Status_Nonsilent), "Other", ctrp_data$Mutation_Status_Nonsilent)
ctrp_data$Hugo_Symbol <- factor(ctrp_data$Hugo_Symbol)
ctrp_data$AUC <- as.numeric(ctrp_data$AUC)
ctrp_data$Color_Nonsilent <- ifelse(ctrp_data$Mutation_Status_Nonsilent == "Other", "slategray3", "steelblue4")
ctrp_data$Color_Nonsilent <- factor(ctrp_data$Color_Nonsilent)
ctrp_data <- filter(ctrp_data, !is.na(ctrp_data$AUC))
ctrp_data$Drug_Gene <- paste0(ctrp_data$Drug, "_", ctrp_data$Hugo_Symbol)
saveRDS(ctrp_data, "./data_munging/rds/ctrp_data_18Q3.rds", compress = "xz")

# GDSC
gdsc <- read.delim("./data_munging/data_drug_auc_gdsc_10_12_2018.csv", sep = "\t", header  = TRUE, row.names = 1, check.names = FALSE)
gdsc <- merge(drug_meta, gdsc, by = "CID")
gdsc_melt <- melt(gdsc, id.vars = c("CID", "Drug"), measure.vars = colnames(gdsc)[3:ncol(gdsc)], variable.name = "accession_id", value.name = "AUC")
gdsc_grid <- expand.grid("accession_id" = unique(gdsc_melt$accession_id), "Hugo_Symbol" = g2p_genes)
gdsc_data <- merge(gdsc_grid, gdsc_melt, by = "accession_id", all.x = TRUE)
gdsc_data <- merge(gdsc_data, ccl_converter, by = "accession_id", all.x = TRUE)
gdsc_data <- merge(gdsc_data, maf_df_g2p, by = c("Hugo_Symbol", "CCLE_Name", "Broad_ID"), all.x = TRUE)
gdsc_data$Mutation_Status_Nonsilent <- ifelse(is.na(gdsc_data$Mutation_Status_Nonsilent), "Other", gdsc_data$Mutation_Status_Nonsilent)
gdsc_data$Hugo_Symbol <- factor(gdsc_data$Hugo_Symbol)
gdsc_data$AUC <- as.numeric(gdsc_data$AUC)
gdsc_data$Color_Nonsilent <- ifelse(gdsc_data$Mutation_Status_Nonsilent == "Other", "paleturquoise3", "turquoise4")
gdsc_data$Color_Nonsilent <- factor(gdsc_data$Color_Nonsilent)
gdsc_data <- filter(gdsc_data, !is.na(gdsc_data$AUC))
gdsc_data$Drug_Gene <- paste0(gdsc_data$Drug, "_", gdsc_data$Hugo_Symbol)
saveRDS(gdsc_data, "./data_munging/rds/gdsc_data_18Q3.rds", compress = "xz")
ccle_data <- readRDS("./data_munging/rds/ccle_data_18Q3.rds")
ccle_data_ptmuts <- filter(ccle_data, Reference_Allele_Length == 1 | Reference_Allele_Length == 0 | is.na(Reference_Allele_Length))
ccle_data_ptmuts$Keep_PtMut_Tests <- ifelse(ccle_data_ptmuts$Reference_Allele_Length == 1 | is.na(ccle_data_ptmuts$Reference_Allele_Length), TRUE, ifelse(ccle_data_ptmuts$Tumor_Seq_Allele1_Length == 1, TRUE, FALSE))
ccle_data_ptmuts <- filter(ccle_data_ptmuts, Keep_PtMut_Tests == TRUE)

ctrp_data <- readRDS("./data_munging/rds/ctrp_data_18Q3.rds")
ctrp_data_ptmuts <- filter(ctrp_data, Reference_Allele_Length == 1 | Reference_Allele_Length == 0 | is.na(Reference_Allele_Length))
ctrp_data_ptmuts$Keep_PtMut_Tests <- ifelse(ctrp_data_ptmuts$Reference_Allele_Length == 1 | is.na(ctrp_data_ptmuts$Reference_Allele_Length), TRUE, ifelse(ctrp_data_ptmuts$Tumor_Seq_Allele1_Length == 1, TRUE, FALSE))
ctrp_data_ptmuts <- filter(ctrp_data_ptmuts, Keep_PtMut_Tests == TRUE)

gdsc_data <- readRDS("./data_munging/rds/gdsc_data_18Q3.rds")
gdsc_data_ptmuts <- filter(gdsc_data, Reference_Allele_Length == 1 | Reference_Allele_Length == 0 | is.na(Reference_Allele_Length))
gdsc_data_ptmuts$Keep_PtMut_Tests <- ifelse(gdsc_data_ptmuts$Reference_Allele_Length == 1 | is.na(gdsc_data_ptmuts$Reference_Allele_Length), TRUE, ifelse(gdsc_data_ptmuts$Tumor_Seq_Allele1_Length == 1, TRUE, FALSE))
gdsc_data_ptmuts <- filter(gdsc_data_ptmuts, Keep_PtMut_Tests == TRUE)

# Filter drug screen datasets for G2P drug-gene associations
ccle_data_g2p <- filter(ccle_data_ptmuts, Drug_Gene %in% g2p_drug_genes)
ctrp_data_g2p <- filter(ctrp_data_ptmuts, Drug_Gene %in% g2p_drug_genes)
gdsc_data_g2p <- filter(gdsc_data_ptmuts, Drug_Gene %in% g2p_drug_genes)

# Filter drug screen datasets for G2P genes in drug-gene associations
ccle_data_g2p_genesfilt <- filter(ccle_data_ptmuts, Hugo_Symbol %in% g2p_genes)
ccle_data_g2p_genesfilt$Hugo_Symbol <- ordered(ccle_data_g2p_genesfilt$Hugo_Symbol, levels = g2p_genes)
ctrp_data_g2p_genesfilt <- filter(ctrp_data_ptmuts, Hugo_Symbol %in% g2p_genes)
ctrp_data_g2p_genesfilt$Hugo_Symbol <- ordered(ctrp_data_g2p_genesfilt$Hugo_Symbol, levels = g2p_genes)
gdsc_data_g2p_genesfilt <- filter(gdsc_data_ptmuts, Hugo_Symbol %in% g2p_genes)
gdsc_data_g2p_genesfilt$Hugo_Symbol <- ordered(gdsc_data_g2p_genesfilt$Hugo_Symbol, levels = g2p_genes)

4 Gene mutation frequency

crispr_data_g2p_summ <- unique(crispr_data_g2p[, c("Hugo_Symbol", "N_Nonsilent_Mutant", "N_Nonsilent_Other")])
crispr_data_g2p_summ$Fraction_Mutant <- crispr_data_g2p_summ$N_Nonsilent_Mutant / (crispr_data_g2p_summ$N_Nonsilent_Mutant + crispr_data_g2p_summ$N_Nonsilent_Other)
crispr_data_g2p_summ$Percent_Mutant <- crispr_data_g2p_summ$Fraction_Mutant * 100
# write.table(crispr_data_g2p_summ, file = "~/Desktop/crispr_data_g2p_summ_20181003.tsv", quote = FALSE, sep = "\t")

5 Wilcoxon tests

5.1 CRISPR: Gene

crispr_signif_g2p_gene <- compare_means(Score ~ Mutation_Status_Nonsilent, group.by = c("Hugo_Symbol"), data = crispr_data_g2p, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_g2p_gene <- adj_signif(crispr_signif_g2p_gene)
crispr_signif_g2p_gene <- crispr_signif_g2p_gene[order(crispr_signif_g2p_gene$p),]
saveRDS(crispr_signif_g2p_gene, "./data_munging/rds/crispr_signif_g2p_gene.rds")
crispr_signif_g2p_gene <- readRDS("./data_munging/rds/crispr_signif_g2p_gene.rds")

crispr_signif_g2p_20gene <- as.character(crispr_signif_g2p_gene$Hugo_Symbol[0:20])

knitr::kable(filter(crispr_signif_g2p_gene, p < 0.1)[, c("Hugo_Symbol", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results for Level A point mutations, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results for Level A point mutations, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol p p.adj p.format p.signif p.signif.adj
KRAS 0.0000000 0.0000000 <2e-16 **** ****
NRAS 0.0000000 0.0000000 <2e-16 **** ****
BRAF 0.0000000 0.0000000 <2e-16 **** ****
TSC2 0.0123492 0.1099118 0.012
NA
JAK2 0.0164691 0.1099118 0.016
NA
UGT1A1 0.0173545 0.1099118 0.017
NA
CYP19A1 0.0266336 0.1445822 0.027
NA
DPYD 0.0395906 0.1880553 0.040
NA 
crispr_data_signif_g2p_gene_filt <- filter(crispr_data_ptmuts, Hugo_Symbol %in% crispr_signif_g2p_20gene)
crispr_data_signif_g2p_gene <- merge(crispr_data_signif_g2p_gene_filt, crispr_signif_g2p_gene, by = "Hugo_Symbol")
crispr_data_signif_g2p_gene$Hugo_Symbol <- ordered(crispr_data_signif_g2p_gene$Hugo_Symbol, levels = crispr_signif_g2p_20gene)
crispr_data_signif_g2p_gene_color <- as.character(crispr_data_signif_g2p_gene$Color_Nonsilent)
names(crispr_data_signif_g2p_gene_color) <- crispr_data_signif_g2p_gene$Mutation_Status_Nonsilent
crispr_data_signif_g2p_gene <- merge(crispr_data_signif_g2p_gene, crispr_data_signif_g2p_gene %>% group_by(Hugo_Symbol) %>% summarize(Text_y = max(Score)), by = "Hugo_Symbol")

crispr_data_signif_g2p_gene_text <- unique(data.frame(p.signif = crispr_data_signif_g2p_gene$p.signif, p.signif.adj = crispr_data_signif_g2p_gene$p.signif.adj,  Hugo_Symbol = crispr_data_signif_g2p_gene$Hugo_Symbol, Text_y = crispr_data_signif_g2p_gene$Text_y))

crispr_data_signif_g2p_gene_plot <- ggplot(data = crispr_data_signif_g2p_gene, aes(x = Mutation_Status_Nonsilent, y = Score)) +
  facet_wrap(~ Hugo_Symbol, nrow = 2, scales = "free_y") +
  geom_jitter(aes(color = Mutation_Status_Nonsilent), alpha = 0.5, width = 0.3) +
  geom_boxplot(color = "black", fill = NA, outlier.shape = NA) +
  geom_hline(yintercept = 0, lty = 2, color = "darkgray") +
  scale_color_manual(values = crispr_data_signif_g2p_gene_color) + 
  geom_text(data = crispr_data_signif_g2p_gene_text, mapping = aes(x = 1.5, y = Text_y, label = p.signif), nudge_y = 0.2) +
  theme(legend.position = "top") +
  labs(y = "CERES Score",
       x = "Lineage",
       color = "Mutation Status",
       title = "CERES scores for select Level A G2P genes",
       subtitle = "Genes are sorted by increasing p-value.")
crispr_data_signif_g2p_gene_plot

# Genes with adjusted p-values < 0.3 are shown.\nBH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001\n
# ggsave("./plots_18Q3/manuscript/crispr_data_signif_g2p_gene_plot.png", crispr_data_signif_g2p_gene_plot, width = 12, height = 7, units = "in")

5.2 CRISPR: Gene and lineage

crispr_signif_g2p_lineage <- compare_means(Score ~ Mutation_Status_Nonsilent, group.by = c("Hugo_Symbol", "group_general_lineage_name"), data = crispr_data_g2p, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_g2p_lineage <- adj_signif(crispr_signif_g2p_lineage)
crispr_signif_g2p_lineage <- crispr_signif_g2p_lineage[order(crispr_signif_g2p_lineage$p),]
saveRDS(crispr_signif_g2p_lineage, "./data_munging/rds/crispr_signif_g2p_lineage.rds")

# write.table(crispr_signif_g2p_lineage, file = "~/Desktop/crispr_signif_g2p_lineage.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_g2p_lineage <- readRDS("./data_munging/rds/crispr_signif_g2p_lineage.rds")

knitr::kable(filter(crispr_signif_g2p_lineage, p < 0.01)[, c("Hugo_Symbol", "group_general_lineage_name", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing non-silent mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing non-silent mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol group_general_lineage_name p p.adj p.format p.signif p.signif.adj
KRAS lung cancer 0.0000000 0.0000030 2.5e-09 **** ****
TP53 lung cancer 0.0000086 0.0049934 8.6e-06 **** **
TP53 central nervous system cancer 0.0000504 0.0195615 5.0e-05 ****
KRAS ovarian cancer 0.0001488 0.0433268 0.00015 ***
TP53 ovarian cancer 0.0002837 0.0661128 0.00028 *** NA
NRAS leukemia 0.0005316 0.1032138 0.00053 *** NA
PIK3CA ovarian cancer 0.0008371 0.1365241 0.00084 *** NA
PIK3CA breast cancer 0.0009842 0.1365241 0.00098 *** NA
KRAS colorectal cancer 0.0010547 0.1365241 0.00105 ** NA
NRAS multiple myeloma 0.0013756 0.1602564 0.00138 ** NA
NRAS skin cancer 0.0016637 0.1762022 0.00166 ** NA
TP53 leukemia 0.0023741 0.2304810 0.00237 ** NA
PTEN ovarian cancer 0.0032141 0.2733311 0.00321 ** NA
TP53 colorectal cancer 0.0036603 0.2733311 0.00366 ** NA
TP53 skin cancer 0.0037227 0.2733311 0.00372 ** NA
VHL kidney cancer 0.0037539 0.2733311 0.00375 ** NA
KRAS stomach cancer 0.0040001 0.2741275 0.00400 ** NA
BRAF skin cancer 0.0054870 0.3419998 0.00549 ** NA
SETD2 kidney cancer 0.0056647 0.3419998 0.00566 ** NA
PTEN central nervous system cancer 0.0059869 0.3419998 0.00599 ** NA
ASXL1 colorectal cancer 0.0061648 0.3419998 0.00616 ** NA
PIK3CA colorectal cancer 0.0070506 0.3733617 0.00705 ** NA
PTEN uterine cancer 0.0080389 0.4071865 0.00804 ** NA 
crispr_data_signif_g2p_lineage <- merge(crispr_data_signif_g2p_gene_filt, crispr_signif_g2p_lineage, by = c("Hugo_Symbol", "group_general_lineage_name"))
crispr_data_signif_g2p_lineage$Hugo_Symbol <- ordered(crispr_data_signif_g2p_lineage$Hugo_Symbol, levels = crispr_signif_g2p_20gene)
crispr_data_signif_g2p_lineage_color <- as.character(crispr_data_signif_g2p_lineage$Color_Nonsilent)
names(crispr_data_signif_g2p_lineage_color) <- crispr_data_signif_g2p_lineage$Mutation_Status_Nonsilent
crispr_data_signif_g2p_lineage <- merge(crispr_data_signif_g2p_lineage, crispr_data_signif_g2p_lineage %>% group_by(Hugo_Symbol) %>% summarize(Text_y = max(Score)), by = "Hugo_Symbol")

crispr_data_signif_g2p_lineage_text <- unique(data.frame(p.signif = crispr_data_signif_g2p_lineage$p.signif, p.signif.adj = crispr_data_signif_g2p_lineage$p.signif.adj,  group_general_lineage_name = crispr_data_signif_g2p_lineage$group_general_lineage_name, Hugo_Symbol = crispr_data_signif_g2p_lineage$Hugo_Symbol, Text_y = crispr_data_signif_g2p_lineage$Text_y))

crispr_data_signif_g2p_lineage_plot <- ggplot(data = crispr_data_signif_g2p_lineage, aes(x = group_general_lineage_name, y = Score)) +
  facet_wrap(~ Hugo_Symbol, nrow = 4, scales = "free_y") +
  geom_point(aes(color = Mutation_Status_Nonsilent), alpha = 0.5) +
  geom_hline(yintercept = 0, lty = 2, color = "darkgray") +
  scale_color_manual(values = crispr_data_signif_g2p_lineage_color) +
  geom_text(data = crispr_data_signif_g2p_lineage_text, mapping = aes(x = group_general_lineage_name, y = Text_y, label = p.signif), angle = 90, nudge_y = 0.1) +
  theme(legend.position = "top", axis.text.x = element_text(angle = 90, hjust = 1, vjust = 1, size = 10)) +
  labs(y = "CERES Score",
       x = "Lineage",
       color = "Mutation Status",
       title = "CERES score by cell line lineage for significant Level A G2P genes",
       subtitle = "Genes with adjusted p-values < 0.3 are shown.\nBH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001\nGenes are sorted by increasing lineage-agnostic p-value.")
crispr_data_signif_g2p_lineage_plot

# ggsave("./plots_18Q3/manuscript/crispr_crispr_data_signif_g2p_lineage_plot.png", crispr_data_signif_g2p_lineage_plot, width = 12, height = 15, units = "in")
crispr_data_signif_g2p_lineage_summ <- crispr_data_signif_g2p_lineage %>% group_by(Hugo_Symbol, group_general_lineage_name, Mutation_Status_Nonsilent) %>% tally()
# write.table(crispr_data_signif_g2p_lineage_summ, file = "~/Desktop/crispr_data_signif_g2p_lineage_summ_20181005.tsv", quote = FALSE, sep = "\t")

knitr::kable(crispr_data_signif_g2p_lineage_summ, caption = "Mutant/Other counts for gene-lineage combinations") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Mutant/Other counts for gene-lineage combinations
Hugo_Symbol group_general_lineage_name Mutation_Status_Nonsilent n
KRAS bone cancer Mutant 1
KRAS bone cancer Other 15
KRAS breast cancer Mutant 2
KRAS breast cancer Other 24
KRAS colorectal cancer Mutant 14
KRAS colorectal cancer Other 13
KRAS esophageal cancer Mutant 1
KRAS esophageal cancer Other 13
KRAS leukemia Mutant 2
KRAS leukemia Other 19
KRAS lung cancer Mutant 22
KRAS lung cancer Other 48
KRAS multiple myeloma Mutant 4
KRAS multiple myeloma Other 12
KRAS ovarian cancer Mutant 9
KRAS ovarian cancer Other 26
KRAS pancreatic cancer Mutant 23
KRAS pancreatic cancer Other 1
KRAS stomach cancer Mutant 5
KRAS stomach cancer Other 10
KRAS urinary bladder cancer Mutant 2
KRAS urinary bladder cancer Other 19
KRAS uterine cancer Mutant 7
KRAS uterine cancer Other 11
NRAS bone cancer Mutant 1
NRAS bone cancer Other 15
NRAS cancer Mutant 2
NRAS cancer Other 16
NRAS central nervous system cancer Mutant 2
NRAS central nervous system cancer Other 43
NRAS head and neck cancer Mutant 1
NRAS head and neck cancer Other 14
NRAS leukemia Mutant 6
NRAS leukemia Other 15
NRAS lung cancer Mutant 5
NRAS lung cancer Other 65
NRAS lymphoma Mutant 2
NRAS lymphoma Other 9
NRAS multiple myeloma Mutant 6
NRAS multiple myeloma Other 10
NRAS peripheral nervous system neoplasm Mutant 2
NRAS peripheral nervous system neoplasm Other 11
NRAS rhabdomyosarcoma Mutant 1
NRAS rhabdomyosarcoma Other 6
NRAS skin cancer Mutant 5
NRAS skin cancer Other 25
NRAS urinary bladder cancer Mutant 4
NRAS urinary bladder cancer Other 17
NRAS uterine cancer Mutant 3
NRAS uterine cancer Other 15
BRAF bone cancer Mutant 1
BRAF bone cancer Other 14
BRAF breast cancer Mutant 3
BRAF breast cancer Other 22
BRAF cancer Mutant 2
BRAF cancer Other 16
BRAF central nervous system cancer Mutant 2
BRAF central nervous system cancer Other 43
BRAF colorectal cancer Mutant 13
BRAF colorectal cancer Other 15
BRAF esophageal cancer Mutant 2
BRAF esophageal cancer Other 12
BRAF liver cancer Mutant 1
BRAF liver cancer Other 15
BRAF lung cancer Mutant 3
BRAF lung cancer Other 66
BRAF multiple myeloma Mutant 2
BRAF multiple myeloma Other 14
BRAF ovarian cancer Mutant 3
BRAF ovarian cancer Other 31
BRAF pancreatic cancer Mutant 2
BRAF pancreatic cancer Other 21
BRAF peripheral nervous system neoplasm Mutant 1
BRAF peripheral nervous system neoplasm Other 12
BRAF skin cancer Mutant 23
BRAF skin cancer Other 8
BRAF stomach cancer Mutant 1
BRAF stomach cancer Other 14
BRAF thyroid cancer Mutant 1
BRAF thyroid cancer Other 2
BRAF urinary bladder cancer Mutant 1
BRAF urinary bladder cancer Other 20
BRAF uterine cancer Mutant 5
BRAF uterine cancer Other 14
TSC2 breast cancer Mutant 1
TSC2 breast cancer Other 25
TSC2 cancer Mutant 1
TSC2 cancer Other 17
TSC2 central nervous system cancer Mutant 1
TSC2 central nervous system cancer Other 43
TSC2 colorectal cancer Mutant 5
TSC2 colorectal cancer Other 22
TSC2 esophageal cancer Mutant 1
TSC2 esophageal cancer Other 13
TSC2 kidney cancer Mutant 1
TSC2 kidney cancer Other 21
TSC2 leukemia Mutant 2
TSC2 leukemia Other 19
TSC2 liver cancer Mutant 2
TSC2 liver cancer Other 14
TSC2 lymphoma Mutant 2
TSC2 lymphoma Other 9
TSC2 multiple myeloma Mutant 2
TSC2 multiple myeloma Other 14
TSC2 pancreatic cancer Mutant 2
TSC2 pancreatic cancer Other 21
TSC2 peripheral nervous system neoplasm Mutant 1
TSC2 peripheral nervous system neoplasm Other 12
TSC2 rhabdomyosarcoma Mutant 1
TSC2 rhabdomyosarcoma Other 6
TSC2 stomach cancer Mutant 2
TSC2 stomach cancer Other 13
TSC2 uterine cancer Mutant 9
TSC2 uterine cancer Other 10
JAK2 colorectal cancer Mutant 1
JAK2 colorectal cancer Other 27
JAK2 esophageal cancer Mutant 1
JAK2 esophageal cancer Other 13
JAK2 leukemia Mutant 1
JAK2 leukemia Other 20
JAK2 lung cancer Mutant 2
JAK2 lung cancer Other 67
JAK2 lymphoma Mutant 1
JAK2 lymphoma Other 10
JAK2 ovarian cancer Mutant 1
JAK2 ovarian cancer Other 34
JAK2 urinary bladder cancer Mutant 2
JAK2 urinary bladder cancer Other 19
JAK2 uterine cancer Mutant 5
JAK2 uterine cancer Other 13
UGT1A1 bone cancer Mutant 2
UGT1A1 bone cancer Other 14
UGT1A1 colorectal cancer Mutant 1
UGT1A1 colorectal cancer Other 25
UGT1A1 esophageal cancer Mutant 1
UGT1A1 esophageal cancer Other 13
UGT1A1 kidney cancer Mutant 1
UGT1A1 kidney cancer Other 21
UGT1A1 lung cancer Mutant 2
UGT1A1 lung cancer Other 67
UGT1A1 skin cancer Mutant 2
UGT1A1 skin cancer Other 28
UGT1A1 stomach cancer Mutant 1
UGT1A1 stomach cancer Other 14
UGT1A1 uterine cancer Mutant 3
UGT1A1 uterine cancer Other 16
CYP19A1 breast cancer Mutant 1
CYP19A1 breast cancer Other 25
CYP19A1 esophageal cancer Mutant 1
CYP19A1 esophageal cancer Other 13
CYP19A1 lymphoma Mutant 1
CYP19A1 lymphoma Other 10
CYP19A1 peripheral nervous system neoplasm Mutant 1
CYP19A1 peripheral nervous system neoplasm Other 12
CYP19A1 stomach cancer Mutant 1
CYP19A1 stomach cancer Other 14
CYP19A1 urinary bladder cancer Mutant 1
CYP19A1 urinary bladder cancer Other 20
CYP19A1 uterine cancer Mutant 2
CYP19A1 uterine cancer Other 16
DPYD bone cancer Mutant 1
DPYD bone cancer Other 15
DPYD breast cancer Mutant 2
DPYD breast cancer Other 24
DPYD central nervous system cancer Mutant 1
DPYD central nervous system cancer Other 44
DPYD colorectal cancer Mutant 4
DPYD colorectal cancer Other 24
DPYD head and neck cancer Mutant 1
DPYD head and neck cancer Other 14
DPYD kidney cancer Mutant 2
DPYD kidney cancer Other 20
DPYD leukemia Mutant 1
DPYD leukemia Other 21
DPYD liver cancer Mutant 1
DPYD liver cancer Other 15
DPYD lung cancer Mutant 4
DPYD lung cancer Other 65
DPYD lymphoma Mutant 1
DPYD lymphoma Other 10
DPYD multiple myeloma Mutant 1
DPYD multiple myeloma Other 15
DPYD ovarian cancer Mutant 2
DPYD ovarian cancer Other 33
DPYD rhabdomyosarcoma Mutant 1
DPYD rhabdomyosarcoma Other 6
DPYD skin cancer Mutant 4
DPYD skin cancer Other 25
DPYD urinary bladder cancer Mutant 1
DPYD urinary bladder cancer Other 20
DPYD uterine cancer Mutant 2
DPYD uterine cancer Other 16
CD274 esophageal cancer Mutant 1
CD274 esophageal cancer Other 13
CD274 liver cancer Mutant 1
CD274 liver cancer Other 15
CD274 ovarian cancer Mutant 1
CD274 ovarian cancer Other 34
CD274 urinary bladder cancer Mutant 1
CD274 urinary bladder cancer Other 20
CD274 uterine cancer Mutant 1
CD274 uterine cancer Other 17
ERBB2 breast cancer Mutant 1
ERBB2 breast cancer Other 25
ERBB2 colorectal cancer Mutant 3
ERBB2 colorectal cancer Other 25
ERBB2 head and neck cancer Mutant 1
ERBB2 head and neck cancer Other 14
ERBB2 kidney cancer Mutant 1
ERBB2 kidney cancer Other 21
ERBB2 leukemia Mutant 1
ERBB2 leukemia Other 20
ERBB2 lung cancer Mutant 3
ERBB2 lung cancer Other 66
ERBB2 ovarian cancer Mutant 2
ERBB2 ovarian cancer Other 33
ERBB2 stomach cancer Mutant 2
ERBB2 stomach cancer Other 13
ERBB2 urinary bladder cancer Mutant 2
ERBB2 urinary bladder cancer Other 20
ERBB2 uterine cancer Mutant 5
ERBB2 uterine cancer Other 14
BRCA2 bone cancer Mutant 1
BRCA2 bone cancer Other 15
BRCA2 breast cancer Mutant 5
BRCA2 breast cancer Other 21
BRCA2 cancer Mutant 3
BRCA2 cancer Other 15
BRCA2 central nervous system cancer Mutant 2
BRCA2 central nervous system cancer Other 43
BRCA2 colorectal cancer Mutant 7
BRCA2 colorectal cancer Other 22
BRCA2 esophageal cancer Mutant 2
BRCA2 esophageal cancer Other 12
BRCA2 kidney cancer Mutant 2
BRCA2 kidney cancer Other 20
BRCA2 leukemia Mutant 3
BRCA2 leukemia Other 18
BRCA2 liver cancer Mutant 1
BRCA2 liver cancer Other 15
BRCA2 lung cancer Mutant 6
BRCA2 lung cancer Other 63
BRCA2 multiple myeloma Mutant 1
BRCA2 multiple myeloma Other 15
BRCA2 ovarian cancer Mutant 2
BRCA2 ovarian cancer Other 33
BRCA2 pancreatic cancer Mutant 1
BRCA2 pancreatic cancer Other 22
BRCA2 skin cancer Mutant 6
BRCA2 skin cancer Other 23
BRCA2 stomach cancer Mutant 2
BRCA2 stomach cancer Other 13
BRCA2 uterine cancer Mutant 7
BRCA2 uterine cancer Other 15
RET bone cancer Mutant 1
RET bone cancer Other 15
RET cancer Mutant 2
RET cancer Other 16
RET central nervous system cancer Mutant 1
RET central nervous system cancer Other 44
RET colorectal cancer Mutant 2
RET colorectal cancer Other 25
RET esophageal cancer Mutant 1
RET esophageal cancer Other 13
RET kidney cancer Mutant 1
RET kidney cancer Other 21
RET leukemia Mutant 1
RET leukemia Other 20
RET lung cancer Mutant 5
RET lung cancer Other 64
RET ovarian cancer Mutant 2
RET ovarian cancer Other 33
RET skin cancer Mutant 2
RET skin cancer Other 27
RET uterine cancer Mutant 2
RET uterine cancer Other 16
ALK breast cancer Mutant 2
ALK breast cancer Other 24
ALK cancer Mutant 2
ALK cancer Other 16
ALK colorectal cancer Mutant 8
ALK colorectal cancer Other 20
ALK esophageal cancer Mutant 2
ALK esophageal cancer Other 12
ALK leukemia Mutant 3
ALK leukemia Other 18
ALK liver cancer Mutant 1
ALK liver cancer Other 15
ALK lung cancer Mutant 7
ALK lung cancer Other 63
ALK lymphoma Mutant 1
ALK lymphoma Other 10
ALK ovarian cancer Mutant 2
ALK ovarian cancer Other 33
ALK pancreatic cancer Mutant 1
ALK pancreatic cancer Other 22
ALK peripheral nervous system neoplasm Mutant 2
ALK peripheral nervous system neoplasm Other 11
ALK skin cancer Mutant 3
ALK skin cancer Other 26
ALK uterine cancer Mutant 3
ALK uterine cancer Other 16
PDGFRA breast cancer Mutant 1
PDGFRA breast cancer Other 25
PDGFRA cancer Mutant 1
PDGFRA cancer Other 17
PDGFRA central nervous system cancer Mutant 1
PDGFRA central nervous system cancer Other 44
PDGFRA colorectal cancer Mutant 3
PDGFRA colorectal cancer Other 24
PDGFRA esophageal cancer Mutant 1
PDGFRA esophageal cancer Other 13
PDGFRA kidney cancer Mutant 1
PDGFRA kidney cancer Other 21
PDGFRA leukemia Mutant 3
PDGFRA leukemia Other 18
PDGFRA lung cancer Mutant 8
PDGFRA lung cancer Other 63
PDGFRA ovarian cancer Mutant 1
PDGFRA ovarian cancer Other 34
PDGFRA pancreatic cancer Mutant 1
PDGFRA pancreatic cancer Other 22
PDGFRA skin cancer Mutant 3
PDGFRA skin cancer Other 26
PDGFRA urinary bladder cancer Mutant 2
PDGFRA urinary bladder cancer Other 19
PDGFRA uterine cancer Mutant 4
PDGFRA uterine cancer Other 14
BRCA1 breast cancer Mutant 2
BRCA1 breast cancer Other 23
BRCA1 colorectal cancer Mutant 5
BRCA1 colorectal cancer Other 23
BRCA1 head and neck cancer Mutant 1
BRCA1 head and neck cancer Other 14
BRCA1 liver cancer Mutant 2
BRCA1 liver cancer Other 14
BRCA1 lung cancer Mutant 4
BRCA1 lung cancer Other 65
BRCA1 lymphoma Mutant 1
BRCA1 lymphoma Other 10
BRCA1 ovarian cancer Mutant 3
BRCA1 ovarian cancer Other 32
BRCA1 pancreatic cancer Mutant 1
BRCA1 pancreatic cancer Other 22
BRCA1 peripheral nervous system neoplasm Mutant 1
BRCA1 peripheral nervous system neoplasm Other 12
BRCA1 skin cancer Mutant 2
BRCA1 skin cancer Other 27
BRCA1 stomach cancer Mutant 2
BRCA1 stomach cancer Other 13
BRCA1 urinary bladder cancer Mutant 2
BRCA1 urinary bladder cancer Other 19
BRCA1 uterine cancer Mutant 5
BRCA1 uterine cancer Other 13
EGFR bone cancer Mutant 1
EGFR bone cancer Other 15
EGFR breast cancer Mutant 1
EGFR breast cancer Other 25
EGFR central nervous system cancer Mutant 4
EGFR central nervous system cancer Other 41
EGFR colorectal cancer Mutant 3
EGFR colorectal cancer Other 26
EGFR esophageal cancer Mutant 4
EGFR esophageal cancer Other 11
EGFR leukemia Mutant 2
EGFR leukemia Other 19
EGFR liver cancer Mutant 1
EGFR liver cancer Other 15
EGFR lung cancer Mutant 7
EGFR lung cancer Other 60
EGFR lymphoma Mutant 1
EGFR lymphoma Other 10
EGFR multiple myeloma Mutant 2
EGFR multiple myeloma Other 14
EGFR ovarian cancer Mutant 2
EGFR ovarian cancer Other 33
EGFR skin cancer Mutant 1
EGFR skin cancer Other 28
EGFR stomach cancer Mutant 1
EGFR stomach cancer Other 14
EGFR uterine cancer Mutant 8
EGFR uterine cancer Other 11
PDCD1 colorectal cancer Mutant 1
PDCD1 colorectal cancer Other 26
PDCD1 lung cancer Mutant 2
PDCD1 lung cancer Other 67
PDCD1 ovarian cancer Mutant 1
PDCD1 ovarian cancer Other 34
PDCD1 uterine cancer Mutant 4
PDCD1 uterine cancer Other 14
NPM1 central nervous system cancer Mutant 2
NPM1 central nervous system cancer Other 43
NPM1 ovarian cancer Mutant 1
NPM1 ovarian cancer Other 34
NPM1 urinary bladder cancer Mutant 1
NPM1 urinary bladder cancer Other 20
NPM1 uterine cancer Mutant 1
NPM1 uterine cancer Other 17
MYD88 bone cancer Mutant 1
MYD88 bone cancer Other 15
MYD88 breast cancer Mutant 1
MYD88 breast cancer Other 25
MYD88 colorectal cancer Mutant 1
MYD88 colorectal cancer Other 26
MYD88 leukemia Mutant 1
MYD88 leukemia Other 20
MYD88 multiple myeloma Mutant 1
MYD88 multiple myeloma Other 15
MYD88 pancreatic cancer Mutant 1
MYD88 pancreatic cancer Other 22
MYD88 urinary bladder cancer Mutant 1
MYD88 urinary bladder cancer Other 20
MYD88 uterine cancer Mutant 3
MYD88 uterine cancer Other 15
ROS1 bone cancer Mutant 1
ROS1 bone cancer Other 15
ROS1 breast cancer Mutant 2
ROS1 breast cancer Other 24
ROS1 cancer Mutant 2
ROS1 cancer Other 16
ROS1 central nervous system cancer Mutant 3
ROS1 central nervous system cancer Other 42
ROS1 colorectal cancer Mutant 4
ROS1 colorectal cancer Other 22
ROS1 esophageal cancer Mutant 3
ROS1 esophageal cancer Other 11
ROS1 head and neck cancer Mutant 3
ROS1 head and neck cancer Other 12
ROS1 kidney cancer Mutant 2
ROS1 kidney cancer Other 20
ROS1 leukemia Mutant 1
ROS1 leukemia Other 20
ROS1 liver cancer Mutant 5
ROS1 liver cancer Other 11
ROS1 lung cancer Mutant 3
ROS1 lung cancer Other 66
ROS1 lymphoma Mutant 2
ROS1 lymphoma Other 9
ROS1 ovarian cancer Mutant 1
ROS1 ovarian cancer Other 34
ROS1 pancreatic cancer Mutant 1
ROS1 pancreatic cancer Other 22
ROS1 peripheral nervous system neoplasm Mutant 1
ROS1 peripheral nervous system neoplasm Other 12
ROS1 skin cancer Mutant 5
ROS1 skin cancer Other 25
ROS1 urinary bladder cancer Mutant 1
ROS1 urinary bladder cancer Other 20
ROS1 uterine cancer Mutant 7
ROS1 uterine cancer Other 12

5.3 CCLE: G2P gene and drug

ccle_signif_g2p <- compare_means(AUC ~ Mutation_Status_Nonsilent, group.by = "Drug_Gene", data = ccle_data_g2p, method = "wilcox.test", p.adjust.method = "BH")
ccle_signif_g2p <- adj_signif(ccle_signif_g2p)
ccle_signif_g2p <- ccle_signif_g2p[order(ccle_signif_g2p$p),]
saveRDS(ccle_signif_g2p, "./data_munging/rds/ccle_signif_g2p_gene.rds")

ccle_signif_g2p_ByDrug <- lapply(unique(ccle_data_g2p_genesfilt$Drug), WilcoxonByDrug, dataset = ccle_data_g2p_genesfilt, data_name = "ccle")
names(ccle_signif_g2p_ByDrug) <- unique(ccle_data_g2p_genesfilt$Drug)
saveRDS(ccle_signif_g2p_ByDrug, "./data_munging/rds/ccle_signif_g2p_ByDrug.rds")
ccle_signif_g2p <- readRDS("./data_munging/rds/ccle_signif_g2p_gene.rds")

knitr::kable(ccle_signif_g2p[, c("Drug_Gene", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "CCLE: Wilcoxon test results for Level A point mutations, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
CCLE: Wilcoxon test results for Level A point mutations, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Drug_Gene p p.adj p.format p.signif p.signif.adj
selumetinib_KRAS 0.0004853 0.0043681 0.00049 *** **
lapatinib_ERBB2 0.0500292 0.1724797 0.05003 NA NA
nilotinib_UGT1A1 0.0574932 0.1724797 0.05749 NA NA
erlotinib_MET 0.1724618 0.3880391 0.17246 NA NA
erlotinib_KRAS 0.3430622 0.4860797 0.34306 NA NA
lapatinib_KRAS 0.3717079 0.4860797 0.37171 NA NA
erlotinib_EGFR 0.3780620 0.4860797 0.37806 NA NA
irinotecan_UGT1A1 0.8309879 0.9348613 0.83099 NA NA
nilotinib_ABL1 0.9989982 0.9989982 0.99900 NA NA 
ccle_signif_g2p_ByDrug <- readRDS("./data_munging/rds/ccle_signif_g2p_ByDrug.rds")
ccle_signif_g2p_ByDrug_all <- rbindlist(ccle_signif_g2p_ByDrug, use.names = TRUE)
ccle_signif_g2p_ByDrug_all <- ccle_signif_g2p_ByDrug_all[order(ccle_signif_g2p_ByDrug_all$p),]

knitr::kable(ccle_signif_g2p_ByDrug_all[, c("Hugo_Symbol", "Drug", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "CCLE: by-drug Wilcoxon test results for Level A point mutations, (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
CCLE: by-drug Wilcoxon test results for Level A point mutations, (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol Drug p p.adj p.format p.signif p.signif.adj
BRAF selumetinib 0.0000000 0.0000016 4.8e-08 **** ****
KRAS selumetinib 0.0004853 0.0082509 0.00049 *** **
KRAS irinotecan 0.0107870 0.2265486 0.011
NA
ERBB3 lapatinib 0.0129696 0.4409649 0.013
NA
FLT3 irinotecan 0.0133264 0.2265486 0.013
NA
ERBB3 nilotinib 0.0219026 0.7446883 0.022
NA
BRAF erlotinib 0.0224018 0.4604309 0.022
NA
KIT lapatinib 0.0299886 0.4872398 0.030
NA
ERBB2 erlotinib 0.0361008 0.4604309 0.036
NA
PDGFB erlotinib 0.0406263 0.4604309 0.041
NA
TSC2 irinotecan 0.0425134 0.4818182 0.043
NA
ERBB2 lapatinib 0.0500292 0.4872398 0.050 NA NA
UGT1A1 nilotinib 0.0574932 0.8315963 0.057 NA NA
ROS1 lapatinib 0.0638294 0.4872398 0.064 NA NA
JAK2 irinotecan 0.0704563 0.5988785 0.070 NA NA
MGMT lapatinib 0.0716529 0.4872398 0.072 NA NA
BRCA2 selumetinib 0.0801455 0.6177935 0.08015 NA NA
BRAF nilotinib 0.0823341 0.8315963 0.082 NA NA
G6PD selumetinib 0.0848649 0.6177935 0.08486 NA NA
DNMT3A lapatinib 0.0910216 0.4905489 0.091 NA NA
IDH2 selumetinib 0.0923191 0.6177935 0.09232 NA NA
ROS1 erlotinib 0.0964090 0.7043128 0.096 NA NA
G6PD nilotinib 0.0978349 0.8315963 0.098 NA NA
AKT1 irinotecan 0.0995560 0.6555760 0.100 NA NA
PTCH1 selumetinib 0.1105610 0.6177935 0.11056 NA NA
BRAF irinotecan 0.1223433 0.6555760 0.122 NA NA
PDGFRA selumetinib 0.1271928 0.6177935 0.12719 NA NA
AKT1 nilotinib 0.1319214 0.8961394 0.132 NA NA
TSC2 lapatinib 0.1353477 0.4905489 0.135 NA NA
BRCA2 lapatinib 0.1454060 0.4905489 0.145 NA NA
DNMT3A selumetinib 0.1475871 0.6272450 0.14759 NA NA
PDGFRA erlotinib 0.1494369 0.7043128 0.149 NA NA
ERBB3 erlotinib 0.1517945 0.7043128 0.152 NA NA
PDGFRA lapatinib 0.1524894 0.4905489 0.152 NA NA
BRAF lapatinib 0.1535431 0.4905489 0.154 NA NA
MGMT erlotinib 0.1554862 0.7043128 0.155 NA NA
RET lapatinib 0.1587070 0.4905489 0.159 NA NA
ESR1 irinotecan 0.1628356 0.6555760 0.163 NA NA
DPYD nilotinib 0.1716871 0.8961394 0.172 NA NA
MET erlotinib 0.1724618 0.7043128 0.172 NA NA
JAK2 selumetinib 0.1725059 0.6516888 0.17251 NA NA
TPMT irinotecan 0.1771488 0.6555760 0.177 NA NA
IDH2 nilotinib 0.1844993 0.8961394 0.184 NA NA
PDGFB lapatinib 0.1902003 0.5389009 0.190 NA NA
JAK2 erlotinib 0.2076226 0.7043128 0.208 NA NA
IDH2 erlotinib 0.2087321 0.7043128 0.209 NA NA
PDGFRB irinotecan 0.2131484 0.6555760 0.213 NA NA
EGFR irinotecan 0.2306603 0.6555760 0.231 NA NA
MGMT irinotecan 0.2444998 0.6555760 0.244 NA NA
ERBB2 nilotinib 0.2469369 0.9412676 0.247 NA NA
JAK2 lapatinib 0.2481605 0.6059107 0.248 NA NA
BRCA2 irinotecan 0.2519045 0.6555760 0.252 NA NA
DPYD irinotecan 0.2565617 0.6555760 0.257 NA NA
PDGFB nilotinib 0.2614178 0.9412676 0.261 NA NA
NPM1 lapatinib 0.2652151 0.6059107 0.265 NA NA
PML lapatinib 0.2673135 0.6059107 0.267 NA NA
DNMT3A irinotecan 0.2699431 0.6555760 0.270 NA NA
BRCA2 erlotinib 0.2904280 0.7043128 0.290 NA NA
KRAS nilotinib 0.2905710 0.9412676 0.291 NA NA
TSC1 selumetinib 0.3026265 0.9776385 0.30263 NA NA
PML erlotinib 0.3071538 0.7043128 0.307 NA NA
RET selumetinib 0.3162948 0.9776385 0.31629 NA NA
JAK2 nilotinib 0.3242397 0.9412676 0.324 NA NA
CYP19A1 irinotecan 0.3281397 0.7321035 0.328 NA NA
KRAS erlotinib 0.3430622 0.7043128 0.343 NA NA
MET irinotecan 0.3595819 0.7321035 0.360 NA NA
CYP19A1 nilotinib 0.3614358 0.9412676 0.361 NA NA
BRCA1 erlotinib 0.3656704 0.7043128 0.366 NA NA
CYP19A1 selumetinib 0.3657355 0.9863877 0.36574 NA NA
MYD88 irinotecan 0.3660517 0.7321035 0.366 NA NA
KRAS lapatinib 0.3717079 0.7898793 0.372 NA NA
MET nilotinib 0.3749787 0.9412676 0.375 NA NA
RET erlotinib 0.3769409 0.7043128 0.377 NA NA
EGFR erlotinib 0.3780620 0.7043128 0.378 NA NA
KIT erlotinib 0.3945572 0.7043128 0.395 NA NA
NPM1 erlotinib 0.4081068 0.7043128 0.408 NA NA
AKT1 erlotinib 0.4089836 0.7043128 0.409 NA NA
TSC2 erlotinib 0.4143017 0.7043128 0.414 NA NA
ALK irinotecan 0.4159284 0.7856426 0.416 NA NA
KIT selumetinib 0.4291048 0.9863877 0.42910 NA NA
BRCA2 nilotinib 0.4329522 0.9412676 0.433 NA NA
DNMT3A nilotinib 0.4393202 0.9412676 0.439 NA NA
ABL1 lapatinib 0.4453225 0.8186706 0.445 NA NA
FLT3 erlotinib 0.4536996 0.7345612 0.454 NA NA
ESR1 selumetinib 0.4556101 0.9863877 0.45561 NA NA
MYD88 lapatinib 0.4587151 0.8186706 0.459 NA NA
ALK lapatinib 0.4609038 0.8186706 0.461 NA NA
PDGFRB nilotinib 0.4795241 0.9412676 0.480 NA NA
ROS1 irinotecan 0.4834670 0.8355783 0.483 NA NA
TPMT erlotinib 0.4842070 0.7483199 0.484 NA NA
DPYD lapatinib 0.4951753 0.8186706 0.495 NA NA
PDGFB irinotecan 0.5117862 0.8355783 0.512 NA NA
ESR1 nilotinib 0.5209092 0.9412676 0.521 NA NA
NPM1 nilotinib 0.5225901 0.9412676 0.523 NA NA
BRCA1 lapatinib 0.5346294 0.8186706 0.535 NA NA
IDH2 irinotecan 0.5383687 0.8355783 0.538 NA NA
ESR1 erlotinib 0.5463206 0.8076043 0.546 NA NA
MET lapatinib 0.5678884 0.8186706 0.568 NA NA
CYP19A1 erlotinib 0.5725012 0.8110433 0.573 NA NA
EGFR selumetinib 0.5763443 0.9863877 0.57634 NA NA
PDGFRB lapatinib 0.5810825 0.8186706 0.581 NA NA
UGT1A1 lapatinib 0.5880643 0.8186706 0.588 NA NA
FLT3 nilotinib 0.5913464 0.9412676 0.591 NA NA
PDGFRA irinotecan 0.5925752 0.8355783 0.593 NA NA
RET nilotinib 0.5975829 0.9412676 0.598 NA NA
TSC1 irinotecan 0.6014923 0.8355783 0.601 NA NA
AKT1 lapatinib 0.6019637 0.8186706 0.602 NA NA
TSC2 selumetinib 0.6111574 0.9863877 0.61116 NA NA
ERBB2 irinotecan 0.6286426 0.8355783 0.629 NA NA
PML irinotecan 0.6294330 0.8355783 0.629 NA NA
G6PD lapatinib 0.6332410 0.8280844 0.633 NA NA
RET irinotecan 0.6389716 0.8355783 0.639 NA NA
TSC1 erlotinib 0.6505989 0.8848145 0.651 NA NA
MET selumetinib 0.6524774 0.9863877 0.65248 NA NA
KIT nilotinib 0.6606331 0.9412676 0.661 NA NA
ABL1 selumetinib 0.6628862 0.9863877 0.66289 NA NA
PDGFRB selumetinib 0.6645199 0.9863877 0.66452 NA NA
ABL1 irinotecan 0.6660281 0.8387021 0.666 NA NA
TPMT nilotinib 0.6679929 0.9412676 0.668 NA NA
ROS1 nilotinib 0.6819122 0.9412676 0.682 NA NA
DPYD erlotinib 0.6840148 0.8944809 0.684 NA NA
DPYD selumetinib 0.6868738 0.9863877 0.68687 NA NA
MYD88 nilotinib 0.6912056 0.9412676 0.691 NA NA
TSC2 nilotinib 0.6921085 0.9412676 0.692 NA NA
CYP19A1 lapatinib 0.6950890 0.8752973 0.695 NA NA
PTCH1 irinotecan 0.7162618 0.8697465 0.716 NA NA
MYD88 selumetinib 0.7246756 0.9863877 0.72468 NA NA
PTCH1 erlotinib 0.7302464 0.9195695 0.730 NA NA
TSC1 lapatinib 0.7401698 0.8933363 0.740 NA NA
PDGFB selumetinib 0.7634636 0.9863877 0.76346 NA NA
G6PD erlotinib 0.7787902 0.9278477 0.779 NA NA
PTCH1 lapatinib 0.7871784 0.8933363 0.787 NA NA
EGFR lapatinib 0.7947247 0.8933363 0.795 NA NA
ALK nilotinib 0.7983903 0.9829706 0.798 NA NA
TSC1 nilotinib 0.7988924 0.9829706 0.799 NA NA
IDH2 lapatinib 0.8145125 0.8933363 0.815 NA NA
BRCA1 irinotecan 0.8210965 0.9114060 0.821 NA NA
ERBB2 selumetinib 0.8218228 0.9863877 0.82182 NA NA
UGT1A1 erlotinib 0.8238031 0.9278477 0.824 NA NA
G6PD irinotecan 0.8254252 0.9114060 0.825 NA NA
ROS1 selumetinib 0.8271911 0.9863877 0.82719 NA NA
ALK selumetinib 0.8278795 0.9863877 0.82788 NA NA
PDGFRB erlotinib 0.8279937 0.9278477 0.828 NA NA
MGMT nilotinib 0.8301945 0.9829706 0.830 NA NA
UGT1A1 irinotecan 0.8309879 0.9114060 0.831 NA NA
ABL1 erlotinib 0.8522490 0.9278477 0.852 NA NA
FLT3 selumetinib 0.8527746 0.9863877 0.85277 NA NA
FLT3 lapatinib 0.8614733 0.9153153 0.861 NA NA
PDGFRA nilotinib 0.8621321 0.9829706 0.862 NA NA
ERBB3 selumetinib 0.8669447 0.9863877 0.86694 NA NA
NPM1 selumetinib 0.8832243 0.9863877 0.88322 NA NA
KIT irinotecan 0.8862231 0.9200633 0.886 NA NA
MYD88 erlotinib 0.8885739 0.9278477 0.889 NA NA
UGT1A1 selumetinib 0.8917738 0.9863877 0.89177 NA NA
NPM1 irinotecan 0.8930026 0.9200633 0.893 NA NA
DNMT3A erlotinib 0.9005581 0.9278477 0.901 NA NA
TPMT lapatinib 0.9089736 0.9365183 0.909 NA NA
PML nilotinib 0.9170484 0.9829706 0.917 NA NA
AKT1 selumetinib 0.9187209 0.9863877 0.91872 NA NA
MGMT selumetinib 0.9254057 0.9863877 0.92541 NA NA
ALK erlotinib 0.9492432 0.9492432 0.949 NA NA
PML selumetinib 0.9504098 0.9863877 0.95041 NA NA
BRCA1 nilotinib 0.9506540 0.9829706 0.951 NA NA
PTCH1 nilotinib 0.9525059 0.9829706 0.953 NA NA
EGFR nilotinib 0.9540597 0.9829706 0.954 NA NA
BRCA1 selumetinib 0.9592240 0.9863877 0.95922 NA NA
ESR1 lapatinib 0.9641363 0.9641363 0.964 NA NA
ERBB3 irinotecan 0.9705439 0.9705439 0.971 NA NA
TPMT selumetinib 0.9863877 0.9863877 0.98639 NA NA
ABL1 nilotinib 0.9989982 0.9989982 0.999 NA NA

5.4 CTRP: G2P gene and drug

ctrp_signif_g2p <- compare_means(AUC ~ Mutation_Status_Nonsilent, group.by = "Drug_Gene", data = ctrp_data_g2p, method = "wilcox.test", p.adjust.method = "BH")
ctrp_signif_g2p <- adj_signif(ctrp_signif_g2p)
ctrp_signif_g2p <- ctrp_signif_g2p[order(ctrp_signif_g2p$p),]
saveRDS(ctrp_signif_g2p, "./data_munging/rds/ctrp_signif_g2p_gene.rds")

ctrp_signif_g2p_ByDrug <- lapply(unique(ctrp_data_g2p_genesfilt$Drug), WilcoxonByDrug, dataset = ctrp_data_g2p_genesfilt, data_name = "CTRP")
names(ctrp_signif_g2p_ByDrug) <- unique(ctrp_data_g2p_genesfilt$Drug)
saveRDS(ctrp_signif_g2p_ByDrug, "./data_munging/rds/ctrp_signif_g2p_ByDrug.rds")
ctrp_signif_g2p <- readRDS("./data_munging/rds/ctrp_signif_g2p_gene.rds")

knitr::kable(ctrp_signif_g2p[, c("Drug_Gene", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "CTRP: Wilcoxon test results for Level A point mutations, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
CTRP: Wilcoxon test results for Level A point mutations, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Drug_Gene p p.adj p.format p.signif p.signif.adj
vemurafenib_BRAF 0.0000005 0.0000236 4.9e-07 **** ****
lapatinib_KRAS 0.0000056 0.0001343 5.6e-06 **** ***
selumetinib_KRAS 0.0002012 0.0032190 0.00020 *** **
dabrafenib_BRAF 0.0002722 0.0032664 0.00027 *** **
gefitinib_KRAS 0.0040210 0.0386011 0.00402 **
imatinib_PDGFRB 0.0054451 0.0435604 0.00545 **
trametinib_BRAF 0.0064941 0.0445309 0.00649 **
cyclophosphamide_ERBB2 0.0089622 0.0537731 0.00896 ** NA
afatinib_KRAS 0.0107829 0.0575089 0.01078
NA
azd_KRAS 0.0127676 0.0612847 0.01277
NA
pazopanib_UGT1A1 0.0341719 0.1491138 0.03417
NA
crizotinib_MET 0.0436200 0.1744801 0.04362
NA
crizotinib_ALK 0.0595488 0.2048823 0.05955 NA NA
fulvestrant_ERBB2 0.0667862 0.2048823 0.06679 NA NA
crizotinib_ROS1 0.0682314 0.2048823 0.06823 NA NA
gefitinib_MET 0.0682941 0.2048823 0.06829 NA NA
dabrafenib_G6PD 0.0952173 0.2688489 0.09522 NA NA
sunitinib_PDGFRA 0.1625269 0.4059909 0.16253 NA NA
temozolomide_MGMT 0.1678755 0.4059909 0.16788 NA NA
vandetanib_RET 0.1691629 0.4059909 0.16916 NA NA
afatinib_ERBB2 0.1962848 0.4486509 0.19628 NA NA
neratinib_ERBB2 0.2066706 0.4509176 0.20667 NA NA
azd_EGFR 0.2197291 0.4585650 0.21973 NA NA
afatinib_EGFR 0.2518397 0.5036794 0.25184 NA NA
ibrutinib_MYD88 0.3177454 0.5850208 0.31775 NA NA
regorafenib_PDGFRA 0.3247031 0.5850208 0.32470 NA NA
imatinib_PDGFB 0.3290742 0.5850208 0.32907 NA NA
regorafenib_KIT 0.4759780 0.8159623 0.47598 NA NA
olaparib_BRCA2 0.5281161 0.8473422 0.52812 NA NA
sunitinib_KIT 0.5322976 0.8473422 0.53230 NA NA
erlotinib_EGFR 0.5472418 0.8473422 0.54724 NA NA
imatinib_ABL1 0.5818558 0.8613135 0.58186 NA NA
cabozantinib_RET 0.6089852 0.8613135 0.60899 NA NA
nilotinib_ABL1 0.6182260 0.8613135 0.61823 NA NA
imatinib_KIT 0.6429646 0.8613135 0.64296 NA NA
erlotinib_MET 0.6459851 0.8613135 0.64599 NA NA
fulvestrant_ESR1 0.6748204 0.8754427 0.67482 NA NA
afatinib_ERBB3 0.6959010 0.8790329 0.69590 NA NA
nilotinib_UGT1A1 0.7429748 0.8953061 0.74297 NA NA
fluorouracil_DPYD 0.7693491 0.8953061 0.76935 NA NA
dasatinib_ABL1 0.7801253 0.8953061 0.78013 NA NA
olaparib_BRCA1 0.8126710 0.8953061 0.81267 NA NA
erlotinib_KRAS 0.8197849 0.8953061 0.81978 NA NA
ruxolitinib_JAK2 0.8206972 0.8953061 0.82070 NA NA
imatinib_PDGFRA 0.8550665 0.9120709 0.85507 NA NA
gefitinib_EGFR 0.8875800 0.9156890 0.88758 NA NA
lapatinib_ERBB2 0.8966122 0.9156890 0.89661 NA NA
belinostat_UGT1A1 0.9223455 0.9223455 0.92235 NA NA 
ctrp_signif_g2p_ByDrug <- readRDS("./data_munging/rds/ctrp_signif_g2p_ByDrug.rds")
ctrp_signif_g2p_ByDrug_all <- rbindlist(ctrp_signif_g2p_ByDrug, use.names = TRUE)
ctrp_signif_g2p_ByDrug_all <- ctrp_signif_g2p_ByDrug_all[order(ctrp_signif_g2p_ByDrug_all$p),]

knitr::kable(ctrp_signif_g2p_ByDrug_all[, c("Hugo_Symbol", "Drug", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "CTRP: by-drug Wilcoxon test results for Level A point mutations, (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
CTRP: by-drug Wilcoxon test results for Level A point mutations, (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol Drug p p.adj p.format p.signif p.signif.adj
BRAF vemurafenib 0.0000005 0.0000167 4.9e-07 **** ****
KRAS lapatinib 0.0000056 0.0001902 5.6e-06 **** ***
KRAS trametinib 0.0000123 0.0004073 1.2e-05 **** ***
KRAS pazopanib 0.0000172 0.0005849 1.7e-05 **** ***
KRAS ibrutinib 0.0000293 0.0009955 2.9e-05 **** ***
MYD88 vemurafenib 0.0000321 0.0005454 3.2e-05 **** ***
KRAS selumetinib 0.0002012 0.0035372 0.00020 *** **
BRAF selumetinib 0.0002081 0.0035372 0.00021 *** **
BRAF olaparib 0.0002505 0.0085179 0.00025 *** **
BRAF dabrafenib 0.0002722 0.0089827 0.00027 *** **
KRAS sunitinib 0.0004277 0.0145425 0.00043 ***
KRAS neratinib 0.0004366 0.0148460 0.00044 ***
EGFR vemurafenib 0.0006405 0.0072593 0.00064 *** **
BRAF afatinib 0.0008985 0.0305482 0.0009 ***
KRAS vandetanib 0.0012628 0.0296819 0.0013 **
DNMT3A vandetanib 0.0017460 0.0296819 0.0017 **
KRAS cabozantinib 0.0020791 0.0706877 0.0021 ** NA
DNMT3A lapatinib 0.0028992 0.0492861 0.0029 **
CYP19A1 vemurafenib 0.0034645 0.0294485 0.00346 **
KRAS olaparib 0.0035982 0.0611698 0.00360 ** NA
KRAS gefitinib 0.0040210 0.1367124 0.004 ** NA
BRAF neratinib 0.0041088 0.0698503 0.00411 ** NA
BRAF pazopanib 0.0041184 0.0700128 0.0041 ** NA
BRAF ibrutinib 0.0051142 0.0869422 0.0051 ** NA
DNMT3A selumetinib 0.0054340 0.0615849 0.00543 ** NA
PDGFRB imatinib 0.0054451 0.1851319 0.0054 ** NA
PML fulvestrant 0.0061906 0.2104795 0.0062 ** NA
BRAF ruxolitinib 0.0062716 0.2132360 0.0063 ** NA
PDGFRB cabozantinib 0.0062762 0.1066960 0.0063 ** NA
BRAF trametinib 0.0064941 0.1071526 0.0065 ** NA
DNMT3A gefitinib 0.0080435 0.1367399 0.008 ** NA
ERBB2 cyclophosphamide 0.0089622 0.3047143 0.009 ** NA
KRAS regorafenib 0.0098050 0.1882113 0.0098 ** NA
ERBB2 cabozantinib 0.0100336 0.1091138 0.0100
NA
KRAS afatinib 0.0107829 0.1699989 0.0108
NA
PML crizotinib 0.0111785 0.3800698 0.011
NA
KRAS vemurafenib 0.0119766 0.0814412 0.01198
NA
MYD88 vandetanib 0.0123446 0.1399058 0.0123
NA
KRAS azd 0.0127676 0.2040480 0.013
NA
ERBB3 cabozantinib 0.0128369 0.1091138 0.0128
NA
BRAF temozolomide 0.0137195 0.4000874 0.014
NA
KIT ibrutinib 0.0142737 0.1617685 0.0143
NA
TPMT sunitinib 0.0149004 0.2533066 0.01490
NA
DNMT3A afatinib 0.0149999 0.1699989 0.0150
NA
PML fluorouracil 0.0155734 0.5294946 0.016
NA
RET azd 0.0156276 0.2040480 0.016
NA
PTCH1 trametinib 0.0159153 0.1750680 0.0159
NA
ROS1 pazopanib 0.0165094 0.1871070 0.0165
NA
PDGFB regorafenib 0.0165670 0.1882113 0.0166
NA
CYP19A1 regorafenib 0.0166069 0.1882113 0.0166
NA
UGT1A1 azd 0.0180042 0.2040480 0.018
NA
BRAF cyclophosphamide 0.0222337 0.3779730 0.022
NA
ALK neratinib 0.0230681 0.2614389 0.02307
NA
DNMT3A trametinib 0.0232930 0.1921671 0.0233
NA
ESR1 imatinib 0.0245120 0.4167035 0.0245
NA
BRCA1 fulvestrant 0.0266668 0.4533360 0.0267
NA
JAK2 temozolomide 0.0271063 0.4000874 0.027
NA
DNMT3A crizotinib 0.0276455 0.3866447 0.028
NA
BRAF dasatinib 0.0300319 0.7302743 0.030
NA
BRAF lapatinib 0.0305016 0.3456843 0.0305
NA
PML selumetinib 0.0317252 0.2169061 0.03173
NA
PDGFRA ibrutinib 0.0333483 0.2315548 0.0333
NA
DNMT3A ibrutinib 0.0340522 0.2315548 0.0341
NA
UGT1A1 pazopanib 0.0341719 0.2904612 0.0342
NA
TSC2 vemurafenib 0.0345796 0.1751927 0.03458
NA
DPYD selumetinib 0.0356352 0.2169061 0.03564
NA
DNMT3A neratinib 0.0356380 0.3029234 0.03564
NA
MET vemurafenib 0.0360691 0.1751927 0.03607
NA
KIT cabozantinib 0.0362427 0.1752370 0.0362
NA
PDGFRB afatinib 0.0366997 0.2708095 0.0367
NA
ROS1 gefitinib 0.0380067 0.3938390 0.038
NA
TPMT selumetinib 0.0382776 0.2169061 0.03828
NA
TSC2 cabozantinib 0.0383320 0.1752370 0.0383
NA
ROS1 cabozantinib 0.0390533 0.1752370 0.0391
NA
BRAF fluorouracil 0.0393299 0.5857145 0.039
NA
KIT afatinib 0.0398249 0.2708095 0.0398
NA
FLT3 cabozantinib 0.0412322 0.1752370 0.0412
NA
MET sunitinib 0.0414057 0.3813132 0.04141
NA
BRAF azd 0.0415725 0.3045006 0.042
NA
DNMT3A erlotinib 0.0426319 0.4945308 0.043
NA
CYP19A1 imatinib 0.0430959 0.4184794 0.0431
NA
FLT3 olaparib 0.0434999 0.3890797 0.04350
NA
MET crizotinib 0.0436200 0.3866447 0.044
NA
ROS1 azd 0.0447795 0.3045006 0.045
NA
ROS1 olaparib 0.0457741 0.3890797 0.04577
NA
TSC1 nilotinib 0.0489600 0.4507292 0.049
NA
BRCA1 belinostat 0.0497327 0.9332615 0.05
NA
PDGFRB regorafenib 0.0500392 0.3550591 0.0500 NA NA
ALK dasatinib 0.0501428 0.7302743 0.050 NA NA
MET selumetinib 0.0507587 0.2252140 0.05076 NA NA
KRAS crizotinib 0.0508005 0.3866447 0.051 NA NA
PTCH1 regorafenib 0.0522146 0.3550591 0.0522 NA NA
PTCH1 vemurafenib 0.0527029 0.2006954 0.05270 NA NA
RET selumetinib 0.0529915 0.2252140 0.05299 NA NA
BRCA1 vemurafenib 0.0531252 0.2006954 0.05313 NA NA
ALK vandetanib 0.0531880 0.4520980 0.0532 NA NA
PDGFRA gefitinib 0.0538638 0.3938390 0.054 NA NA
MET nilotinib 0.0553000 0.4507292 0.055 NA NA
JAK2 nilotinib 0.0558396 0.4507292 0.056 NA NA
TPMT imatinib 0.0564058 0.4184794 0.0564 NA NA
BRCA2 temozolomide 0.0567584 0.4000874 0.057 NA NA
MYD88 temozolomide 0.0574402 0.4000874 0.057 NA NA
NPM1 lapatinib 0.0576027 0.4247950 0.0576 NA NA
MGMT dabrafenib 0.0578528 0.6995857 0.05785 NA NA
IDH2 olaparib 0.0581368 0.3953299 0.05814 NA NA
PTCH1 temozolomide 0.0588364 0.4000874 0.059 NA NA
ALK crizotinib 0.0595488 0.3866447 0.060 NA NA
JAK2 cabozantinib 0.0601835 0.1902348 0.0602 NA NA
ABL1 cyclophosphamide 0.0608027 0.6890967 0.061 NA NA
PDGFRB sunitinib 0.0609026 0.3813132 0.06090 NA NA
PTCH1 imatinib 0.0615411 0.4184794 0.0615 NA NA
DPYD neratinib 0.0622681 0.4234232 0.06227 NA NA
PDGFRA lapatinib 0.0624698 0.4247950 0.0625 NA NA
PTCH1 erlotinib 0.0624883 0.4945308 0.062 NA NA
MET cabozantinib 0.0628214 0.1902348 0.0628 NA NA
CYP19A1 dasatinib 0.0644360 0.7302743 0.064 NA NA
PTCH1 sunitinib 0.0653514 0.3813132 0.06535 NA NA
UGT1A1 cabozantinib 0.0659861 0.1902348 0.0660 NA NA
ERBB2 fulvestrant 0.0667862 0.7405746 0.0668 NA NA
AKT1 cabozantinib 0.0671417 0.1902348 0.0671 NA NA
DNMT3A sunitinib 0.0672906 0.3813132 0.06729 NA NA
EGFR regorafenib 0.0674563 0.3598309 0.0675 NA NA
ROS1 crizotinib 0.0682314 0.3866447 0.068 NA NA
MET gefitinib 0.0682941 0.3938390 0.068 NA NA
ROS1 erlotinib 0.0687863 0.4945308 0.069 NA NA
JAK2 erlotinib 0.0694206 0.4945308 0.069 NA NA
JAK2 gefitinib 0.0695010 0.3938390 0.070 NA NA
KRAS ruxolitinib 0.0698118 0.9314078 0.0698 NA NA
KRAS nilotinib 0.0700538 0.4507292 0.070 NA NA
TPMT afatinib 0.0710783 0.3521922 0.0711 NA NA
DPYD azd 0.0712184 0.4035707 0.071 NA NA
ALK afatinib 0.0725102 0.3521922 0.0725 NA NA
PDGFRA vemurafenib 0.0735826 0.2190817 0.07358 NA NA
G6PD regorafenib 0.0740828 0.3598309 0.0741 NA NA
KIT selumetinib 0.0746874 0.2821523 0.07469 NA NA
NPM1 pazopanib 0.0750765 0.5105199 0.0751 NA NA
TPMT cabozantinib 0.0751475 0.1965396 0.0751 NA NA
TSC1 vemurafenib 0.0759962 0.2190817 0.07600 NA NA
PML vemurafenib 0.0789218 0.2190817 0.07892 NA NA
TSC1 fluorouracil 0.0794633 0.5857145 0.079 NA NA
UGT1A1 sunitinib 0.0800633 0.3888788 0.08006 NA NA
DNMT3A fluorouracil 0.0810167 0.5857145 0.081 NA NA
G6PD nilotinib 0.0817599 0.4507292 0.082 NA NA
PDGFRA erlotinib 0.0818940 0.4945308 0.082 NA NA
ERBB2 dabrafenib 0.0822127 0.6995857 0.08221 NA NA
RET afatinib 0.0832455 0.3537932 0.0832 NA NA
PDGFRB trametinib 0.0837396 0.4658024 0.0837 NA NA
BRCA2 vemurafenib 0.0837665 0.2190817 0.08377 NA NA
MYD88 nilotinib 0.0840035 0.4507292 0.084 NA NA
BRCA1 azd 0.0871239 0.4231730 0.087 NA NA
TPMT crizotinib 0.0889926 0.4322498 0.089 NA NA
KIT trametinib 0.0892804 0.4658024 0.0893 NA NA
RET fluorouracil 0.0904485 0.5857145 0.090 NA NA
BRCA2 pazopanib 0.0923309 0.5232086 0.0923 NA NA
TPMT dasatinib 0.0925509 0.7866823 0.093 NA NA
ROS1 nilotinib 0.0927972 0.4507292 0.093 NA NA
PDGFRA temozolomide 0.0937160 0.5310575 0.094 NA NA
G6PD dabrafenib 0.0952173 0.6995857 0.09522 NA NA
RET cyclophosphamide 0.0968718 0.8234099 0.097 NA NA
DNMT3A olaparib 0.0996068 0.5644383 0.09961 NA NA
DPYD afatinib 0.1034804 0.3909259 0.1035 NA NA
ABL1 trametinib 0.1038508 0.4658024 0.1039 NA NA
MGMT imatinib 0.1047274 0.5934553 0.1047 NA NA
DPYD vandetanib 0.1050806 0.6681475 0.1051 NA NA
PDGFB ibrutinib 0.1088089 0.5415524 0.1088 NA NA
MGMT erlotinib 0.1093037 0.4945308 0.109 NA NA
AKT1 crizotinib 0.1095589 0.4656252 0.110 NA NA
AKT1 azd 0.1125486 0.4576031 0.113 NA NA
RET trametinib 0.1129218 0.4658024 0.1129 NA NA
UGT1A1 dabrafenib 0.1142522 0.6995857 0.11425 NA NA
ESR1 fluorouracil 0.1147435 0.5857145 0.115 NA NA
DNMT3A pazopanib 0.1153640 0.5603392 0.1154 NA NA
PDGFB vemurafenib 0.1163543 0.2825748 0.11635 NA NA
PTCH1 lapatinib 0.1167103 0.6029902 0.1167 NA NA
AKT1 sunitinib 0.1198936 0.4988051 0.11989 NA NA
PDGFRA azd 0.1211302 0.4576031 0.121 NA NA
BRCA1 nilotinib 0.1242641 0.4564231 0.124 NA NA
TPMT neratinib 0.1244008 0.6384596 0.12440 NA NA
TSC1 cabozantinib 0.1252422 0.3041597 0.1252 NA NA
PML gefitinib 0.1255530 0.6098289 0.126 NA NA
ERBB2 fluorouracil 0.1257464 0.5857145 0.126 NA NA
CYP19A1 ibrutinib 0.1269984 0.5415524 0.1270 NA NA
CYP19A1 dabrafenib 0.1271974 0.6995857 0.12720 NA NA
ALK ibrutinib 0.1274241 0.5415524 0.1274 NA NA
PTCH1 nilotinib 0.1277181 0.4564231 0.128 NA NA
NPM1 erlotinib 0.1307567 0.4945308 0.131 NA NA
EGFR trametinib 0.1316155 0.4825901 0.1316 NA NA
NPM1 sunitinib 0.1320366 0.4988051 0.13204 NA NA
ROS1 regorafenib 0.1323222 0.5623696 0.1323 NA NA
JAK2 vemurafenib 0.1329072 0.3012562 0.13291 NA NA
BRAF imatinib 0.1350841 0.6561226 0.1351 NA NA
BRCA2 neratinib 0.1369451 0.6384596 0.13695 NA NA
PTCH1 olaparib 0.1376429 0.6685514 0.13764 NA NA
TPMT ruxolitinib 0.1408874 0.9314078 0.1409 NA NA
UGT1A1 selumetinib 0.1423368 0.4163122 0.14234 NA NA
MGMT fluorouracil 0.1432551 0.5857145 0.143 NA NA
BRCA2 erlotinib 0.1458082 0.4945308 0.146 NA NA
ALK erlotinib 0.1462901 0.4945308 0.146 NA NA
FLT3 nilotinib 0.1476259 0.4564231 0.148 NA NA
DPYD nilotinib 0.1476663 0.4564231 0.148 NA NA
G6PD trametinib 0.1496537 0.4938571 0.1497 NA NA
TSC1 sunitinib 0.1518085 0.5023558 0.15181 NA NA
AKT1 neratinib 0.1528627 0.6384596 0.15286 NA NA
DPYD fulvestrant 0.1532097 0.7405746 0.1532 NA NA
ERBB2 erlotinib 0.1533325 0.4945308 0.153 NA NA
G6PD selumetinib 0.1551003 0.4163122 0.15510 NA NA
PDGFRB selumetinib 0.1562725 0.4163122 0.15627 NA NA
ALK azd 0.1572449 0.5346326 0.157 NA NA
FLT3 vemurafenib 0.1577275 0.3259020 0.15773 NA NA
NPM1 crizotinib 0.1583886 0.5983568 0.158 NA NA
CYP19A1 fluorouracil 0.1588485 0.5857145 0.159 NA NA
DNMT3A dasatinib 0.1589015 0.8981810 0.159 NA NA
ALK selumetinib 0.1591782 0.4163122 0.15918 NA NA
KIT erlotinib 0.1599953 0.4945308 0.160 NA NA
CYP19A1 belinostat 0.1605668 0.9332615 0.16 NA NA
UGT1A1 olaparib 0.1608514 0.6836186 0.16085 NA NA
ABL1 ruxolitinib 0.1623760 0.9314078 0.1624 NA NA
PDGFRA sunitinib 0.1625269 0.5023558 0.16253 NA NA
AKT1 vemurafenib 0.1629510 0.3259020 0.16295 NA NA
CYP19A1 nilotinib 0.1633178 0.4627337 0.163 NA NA
RET lapatinib 0.1635140 0.6029902 0.1635 NA NA
ROS1 ruxolitinib 0.1652562 0.9314078 0.1653 NA NA
TPMT gefitinib 0.1653865 0.6634491 0.165 NA NA
CYP19A1 cyclophosphamide 0.1664216 0.8874508 0.166 NA NA
MET cyclophosphamide 0.1671638 0.8874508 0.167 NA NA
MGMT temozolomide 0.1678755 0.7065822 0.168 NA NA
ERBB2 dasatinib 0.1679727 0.8981810 0.168 NA NA
TSC1 neratinib 0.1690040 0.6384596 0.16900 NA NA
RET vandetanib 0.1691629 0.6681475 0.1692 NA NA
ROS1 afatinib 0.1715284 0.5539450 0.1715 NA NA
ALK fluorouracil 0.1722690 0.5857145 0.172 NA NA
IDH2 fulvestrant 0.1760724 0.7405746 0.1761 NA NA
BRCA2 selumetinib 0.1762300 0.4279871 0.17623 NA NA
IDH2 imatinib 0.1770530 0.6851658 0.1771 NA NA
TSC1 dabrafenib 0.1796741 0.8398900 0.17967 NA NA
ABL1 belinostat 0.1800207 0.9332615 0.18 NA NA
G6PD gefitinib 0.1809687 0.6634491 0.181 NA NA
PDGFRB pazopanib 0.1809713 0.6888301 0.1810 NA NA
ALK lapatinib 0.1820481 0.6029902 0.1820 NA NA
KIT pazopanib 0.1823374 0.6888301 0.1823 NA NA
UGT1A1 cyclophosphamide 0.1827105 0.8874508 0.183 NA NA
ESR1 trametinib 0.1839575 0.5233822 0.1840 NA NA
CYP19A1 sunitinib 0.1840148 0.5213753 0.18401 NA NA
RET ruxolitinib 0.1851983 0.9314078 0.1852 NA NA
MET fulvestrant 0.1858576 0.7405746 0.1859 NA NA
ALK temozolomide 0.1882566 0.7065822 0.188 NA NA
PDGFRA trametinib 0.1903208 0.5233822 0.1903 NA NA
MGMT vandetanib 0.1909706 0.6681475 0.1910 NA NA
JAK2 azd 0.1912155 0.5910299 0.191 NA NA
ERBB2 afatinib 0.1962848 0.5539450 0.1963 NA NA
DPYD vemurafenib 0.1972116 0.3725107 0.19721 NA NA
BRCA1 cabozantinib 0.1984399 0.4341695 0.1984 NA NA
IDH2 afatinib 0.2002219 0.5539450 0.2002 NA NA
BRCA1 sunitinib 0.2029629 0.5308261 0.20296 NA NA
PTCH1 cabozantinib 0.2043150 0.4341695 0.2043 NA NA
KRAS temozolomide 0.2044885 0.7065822 0.204 NA NA
RET gefitinib 0.2049189 0.6634491 0.205 NA NA
JAK2 vandetanib 0.2056568 0.6681475 0.2057 NA NA
TPMT lapatinib 0.2063631 0.6029902 0.2064 NA NA
ERBB2 neratinib 0.2066706 0.7012154 0.20667 NA NA
ABL1 temozolomide 0.2078183 0.7065822 0.208 NA NA
G6PD vandetanib 0.2082743 0.6681475 0.2083 NA NA
PDGFB ruxolitinib 0.2112854 0.9314078 0.2113 NA NA
JAK2 regorafenib 0.2116803 0.7996810 0.2117 NA NA
MGMT afatinib 0.2118025 0.5539450 0.2118 NA NA
TSC1 ibrutinib 0.2141178 0.7299037 0.2141 NA NA
RET erlotinib 0.2156089 0.5141116 0.216 NA NA
MET trametinib 0.2171377 0.5469503 0.2171 NA NA
TPMT olaparib 0.2193329 0.6914387 0.21933 NA NA
MET vandetanib 0.2195544 0.6681475 0.2196 NA NA
EGFR azd 0.2197291 0.5979347 0.220 NA NA
UGT1A1 crizotinib 0.2205241 0.6987047 0.221 NA NA
PDGFRA cabozantinib 0.2210144 0.4420288 0.2210 NA NA
AKT1 erlotinib 0.2216745 0.5141116 0.222 NA NA
ROS1 lapatinib 0.2232310 0.6029902 0.2232 NA NA
AKT1 vandetanib 0.2252466 0.6681475 0.2252 NA NA
DPYD erlotinib 0.2260758 0.5141116 0.226 NA NA
IDH2 erlotinib 0.2268140 0.5141116 0.227 NA NA
KIT neratinib 0.2272109 0.7012154 0.22721 NA NA
TSC1 azd 0.2297525 0.5979347 0.230 NA NA
NPM1 fulvestrant 0.2309231 0.7405746 0.2309 NA NA
MGMT dasatinib 0.2320758 0.8981810 0.232 NA NA
FLT3 ibrutinib 0.2324658 0.7299037 0.2325 NA NA
ROS1 fulvestrant 0.2343543 0.7405746 0.2344 NA NA
TPMT vandetanib 0.2358167 0.6681475 0.2358 NA NA
DPYD gefitinib 0.2382705 0.6634491 0.238 NA NA
RET crizotinib 0.2389903 0.6987047 0.239 NA NA
MGMT lapatinib 0.2397677 0.6029902 0.2398 NA NA
ROS1 trametinib 0.2403793 0.5469503 0.2404 NA NA
MYD88 dabrafenib 0.2408578 0.8398900 0.24086 NA NA
AKT1 lapatinib 0.2447114 0.6029902 0.2447 NA NA
PML nilotinib 0.2462058 0.6439228 0.246 NA NA
KIT crizotinib 0.2466017 0.6987047 0.247 NA NA
PDGFB pazopanib 0.2466642 0.7489184 0.2467 NA NA
RET neratinib 0.2474878 0.7012154 0.24749 NA NA
PDGFB azd 0.2475165 0.5979347 0.248 NA NA
MGMT vemurafenib 0.2488404 0.4452933 0.24884 NA NA
MGMT gefitinib 0.2498351 0.6634491 0.250 NA NA
MET olaparib 0.2511607 0.6914387 0.25116 NA NA
G6PD sunitinib 0.2518013 0.5738528 0.25180 NA NA
EGFR afatinib 0.2518397 0.6116107 0.2518 NA NA
TPMT trametinib 0.2520717 0.5469503 0.2521 NA NA
JAK2 cyclophosphamide 0.2532387 0.9645613 0.253 NA NA
TSC2 cyclophosphamide 0.2553250 0.9645613 0.255 NA NA
UGT1A1 regorafenib 0.2573217 0.8332306 0.2573 NA NA
MGMT ruxolitinib 0.2579077 0.9314078 0.2579 NA NA
NPM1 ibrutinib 0.2580132 0.7299037 0.2580 NA NA
PDGFRB ibrutinib 0.2584041 0.7299037 0.2584 NA NA
TPMT pazopanib 0.2589317 0.7489184 0.2589 NA NA
AKT1 olaparib 0.2619009 0.6914387 0.26190 NA NA
IDH2 lapatinib 0.2631941 0.6029902 0.2632 NA NA
PML olaparib 0.2632816 0.6914387 0.26328 NA NA
MET pazopanib 0.2643242 0.7489184 0.2643 NA NA
TSC2 azd 0.2666255 0.5979347 0.267 NA NA
TSC1 dasatinib 0.2684477 0.8981810 0.268 NA NA
MYD88 cabozantinib 0.2705198 0.5109818 0.2705 NA NA
AKT1 gefitinib 0.2710276 0.6634491 0.271 NA NA
MYD88 lapatinib 0.2727428 0.6029902 0.2727 NA NA
JAK2 fulvestrant 0.2731170 0.7405746 0.2731 NA NA
CYP19A1 lapatinib 0.2738891 0.6029902 0.2739 NA NA
ERBB2 gefitinib 0.2741193 0.6634491 0.274 NA NA
PDGFRA selumetinib 0.2749493 0.6232184 0.27495 NA NA
KRAS fulvestrant 0.2766131 0.7405746 0.2766 NA NA
TSC1 imatinib 0.2772743 0.6851658 0.2773 NA NA
MGMT trametinib 0.2783193 0.5469503 0.2783 NA NA
ALK imatinib 0.2787671 0.6851658 0.2788 NA NA
MGMT fulvestrant 0.2788073 0.7405746 0.2788 NA NA
AKT1 ibrutinib 0.2790808 0.7299037 0.2791 NA NA
ROS1 sunitinib 0.2791187 0.5738528 0.27912 NA NA
FLT3 dabrafenib 0.2794287 0.8398900 0.27943 NA NA
ABL1 fulvestrant 0.2803031 0.7405746 0.2803 NA NA
TSC2 sunitinib 0.2822305 0.5738528 0.28223 NA NA
ERBB2 trametinib 0.2825628 0.5469503 0.2826 NA NA
CYP19A1 fulvestrant 0.2831609 0.7405746 0.2832 NA NA
BRCA1 lapatinib 0.2837601 0.6029902 0.2838 NA NA
EGFR vandetanib 0.2853916 0.6968774 0.2854 NA NA
PTCH1 dasatinib 0.2856356 0.8981810 0.286 NA NA
ERBB3 sunitinib 0.2869264 0.5738528 0.28693 NA NA
ESR1 olaparib 0.2881802 0.6914387 0.28818 NA NA
TPMT fluorouracil 0.2892300 0.8392181 0.289 NA NA
MET regorafenib 0.2896133 0.8332306 0.2896 NA NA
PDGFRB neratinib 0.2911880 0.7411173 0.29119 NA NA
IDH2 vandetanib 0.2935755 0.6968774 0.2936 NA NA
ERBB2 imatinib 0.2961755 0.6851658 0.2962 NA NA
NPM1 azd 0.2976666 0.5979347 0.298 NA NA
DPYD belinostat 0.2978744 0.9332615 0.30 NA NA
UGT1A1 ruxolitinib 0.2979995 0.9314078 0.2980 NA NA
TSC2 dasatinib 0.2997123 0.8981810 0.300 NA NA
MGMT nilotinib 0.3000538 0.6835150 0.300 NA NA
NPM1 cabozantinib 0.3013980 0.5150108 0.3014 NA NA
ABL1 crizotinib 0.3022158 0.7904106 0.302 NA NA
PTCH1 azd 0.3023110 0.5979347 0.302 NA NA
ABL1 cabozantinib 0.3029475 0.5150108 0.3029 NA NA
CYP19A1 olaparib 0.3043830 0.6914387 0.30438 NA NA
ESR1 gefitinib 0.3045810 0.6634491 0.305 NA NA
PDGFB neratinib 0.3051659 0.7411173 0.30517 NA NA
ALK vemurafenib 0.3064787 0.5210137 0.30648 NA NA
PDGFRA vandetanib 0.3074459 0.6968774 0.3074 NA NA
AKT1 afatinib 0.3079994 0.6638151 0.3080 NA NA
BRAF belinostat 0.3085030 0.9332615 0.31 NA NA
TSC1 selumetinib 0.3094377 0.6575550 0.30944 NA NA
DNMT3A imatinib 0.3100619 0.6851658 0.3101 NA NA
TPMT erlotinib 0.3109754 0.6608228 0.311 NA NA
PML trametinib 0.3122459 0.5469503 0.3122 NA NA
ALK trametinib 0.3149108 0.5469503 0.3149 NA NA
RET dasatinib 0.3153187 0.8981810 0.315 NA NA
UGT1A1 imatinib 0.3163786 0.6851658 0.3164 NA NA
MYD88 gefitinib 0.3170349 0.6634491 0.317 NA NA
EGFR olaparib 0.3170528 0.6914387 0.31705 NA NA
MYD88 ibrutinib 0.3177454 0.7476014 0.3177 NA NA
FLT3 pazopanib 0.3190784 0.8345127 0.3191 NA NA
ROS1 temozolomide 0.3206533 0.7644487 0.321 NA NA
TSC2 nilotinib 0.3227612 0.6835150 0.323 NA NA
BRCA1 temozolomide 0.3234468 0.7644487 0.323 NA NA
PDGFRA regorafenib 0.3247031 0.8332306 0.3247 NA NA
G6PD cabozantinib 0.3251186 0.5263825 0.3251 NA NA
ALK olaparib 0.3253829 0.6914387 0.32538 NA NA
IDH2 dabrafenib 0.3256308 0.8398900 0.32563 NA NA
PDGFRB ruxolitinib 0.3271325 0.9314078 0.3271 NA NA
PDGFB imatinib 0.3290742 0.6851658 0.3291 NA NA
BRCA1 regorafenib 0.3293828 0.8332306 0.3294 NA NA
ABL1 fluorouracil 0.3296373 0.8392181 0.330 NA NA
UGT1A1 ibrutinib 0.3298241 0.7476014 0.3298 NA NA
ERBB3 imatinib 0.3301067 0.6851658 0.3301 NA NA
ESR1 lapatinib 0.3306838 0.6259186 0.3307 NA NA
TPMT azd 0.3307900 0.5979347 0.331 NA NA
MET lapatinib 0.3313687 0.6259186 0.3314 NA NA
KIT gefitinib 0.3317245 0.6634491 0.332 NA NA
MYD88 afatinib 0.3320613 0.6638151 0.3321 NA NA
PDGFRA fluorouracil 0.3334048 0.8392181 0.333 NA NA
IDH2 neratinib 0.3337619 0.7446930 0.33376 NA NA
MYD88 azd 0.3389331 0.5979347 0.339 NA NA
PML imatinib 0.3392370 0.6851658 0.3392 NA NA
ERBB2 selumetinib 0.3397053 0.6794106 0.33971 NA NA
ABL1 afatinib 0.3423060 0.6638151 0.3423 NA NA
KRAS imatinib 0.3425829 0.6851658 0.3426 NA NA
TSC1 regorafenib 0.3430949 0.8332306 0.3431 NA NA
ESR1 nilotinib 0.3452748 0.6835150 0.345 NA NA
NPM1 fluorouracil 0.3455604 0.8392181 0.346 NA NA
BRAF nilotinib 0.3473234 0.6835150 0.347 NA NA
ERBB3 belinostat 0.3479934 0.9332615 0.35 NA NA
IDH2 cabozantinib 0.3499020 0.5407576 0.3499 NA NA
NPM1 neratinib 0.3504438 0.7446930 0.35044 NA NA
TSC2 afatinib 0.3514315 0.6638151 0.3514 NA NA
KRAS dasatinib 0.3522956 0.8981810 0.352 NA NA
ERBB3 cyclophosphamide 0.3561490 0.9793614 0.356 NA NA
IDH2 azd 0.3568667 0.5979347 0.357 NA NA
PDGFB dasatinib 0.3571989 0.8981810 0.357 NA NA
NPM1 dabrafenib 0.3577714 0.8398900 0.35777 NA NA
BRAF fulvestrant 0.3578334 0.8690240 0.3578 NA NA
ALK gefitinib 0.3595266 0.6791058 0.360 NA NA
NPM1 temozolomide 0.3616494 0.7644487 0.362 NA NA
PDGFB nilotinib 0.3618609 0.6835150 0.362 NA NA
UGT1A1 temozolomide 0.3627228 0.7644487 0.363 NA NA
MET dabrafenib 0.3629609 0.8398900 0.36296 NA NA
PDGFRB belinostat 0.3636843 0.9332615 0.36 NA NA
ERBB3 lapatinib 0.3667711 0.6475307 0.3668 NA NA
IDH2 vemurafenib 0.3678300 0.5955342 0.36783 NA NA
TPMT regorafenib 0.3679816 0.8340916 0.3680 NA NA
ERBB3 azd 0.3693126 0.5979347 0.369 NA NA
JAK2 afatinib 0.3737944 0.6674950 0.3738 NA NA
FLT3 temozolomide 0.3743174 0.7644487 0.374 NA NA
ROS1 cyclophosphamide 0.3774686 0.9793614 0.377 NA NA
ABL1 ibrutinib 0.3777841 0.8027912 0.3778 NA NA
PDGFRA pazopanib 0.3783240 0.9187869 0.3783 NA NA
EGFR lapatinib 0.3809004 0.6475307 0.3809 NA NA
PML sunitinib 0.3810854 0.7198280 0.38109 NA NA
BRCA2 trametinib 0.3821071 0.6252529 0.3821 NA NA
EGFR ruxolitinib 0.3825743 0.9314078 0.3826 NA NA
PML dasatinib 0.3857611 0.8981810 0.386 NA NA
MGMT neratinib 0.3926360 0.7852720 0.39264 NA NA
PML afatinib 0.3926441 0.6674950 0.3926 NA NA
BRAF cabozantinib 0.3934771 0.5686724 0.3935 NA NA
ROS1 vemurafenib 0.3937304 0.6084924 0.39373 NA NA
ERBB3 dabrafenib 0.3967421 0.8398900 0.39674 NA NA
TPMT cyclophosphamide 0.3978413 0.9793614 0.398 NA NA
RET belinostat 0.3979105 0.9332615 0.40 NA NA
ALK dabrafenib 0.3998459 0.8398900 0.39985 NA NA
PDGFRA dasatinib 0.4012694 0.8981810 0.401 NA NA
NPM1 vandetanib 0.4033915 0.8222586 0.4034 NA NA
PML ruxolitinib 0.4077983 0.9314078 0.4078 NA NA
AKT1 trametinib 0.4079935 0.6252529 0.4080 NA NA
KIT dabrafenib 0.4097454 0.8398900 0.40975 NA NA
RET regorafenib 0.4107421 0.8471493 0.4107 NA NA
ERBB3 vandetanib 0.4111293 0.8222586 0.4111 NA NA
KRAS dabrafenib 0.4111914 0.8398900 0.41119 NA NA
DPYD temozolomide 0.4129554 0.7644487 0.413 NA NA
FLT3 lapatinib 0.4146955 0.6714118 0.4147 NA NA
ESR1 temozolomide 0.4151428 0.7644487 0.415 NA NA
BRCA2 cabozantinib 0.4162945 0.5686724 0.4163 NA NA
TSC2 trametinib 0.4168352 0.6252529 0.4168 NA NA
PML erlotinib 0.4170584 0.8218115 0.417 NA NA
CYP19A1 cabozantinib 0.4181415 0.5686724 0.4181 NA NA
JAK2 crizotinib 0.4182454 0.9896386 0.418 NA NA
ALK fulvestrant 0.4191815 0.9110805 0.4192 NA NA
PTCH1 selumetinib 0.4194027 0.7472482 0.41940 NA NA
EGFR dasatinib 0.4226734 0.8981810 0.423 NA NA
BRCA2 azd 0.4237037 0.6548149 0.424 NA NA
KIT cyclophosphamide 0.4256579 0.9793614 0.426 NA NA
ESR1 ibrutinib 0.4291778 0.8107592 0.4292 NA NA
EGFR ibrutinib 0.4292255 0.8107592 0.4292 NA NA
NPM1 selumetinib 0.4302455 0.7472482 0.43025 NA NA
PML pazopanib 0.4313542 0.9261994 0.4314 NA NA
TPMT dabrafenib 0.4326706 0.8398900 0.43267 NA NA
PDGFRB fluorouracil 0.4341969 0.8677771 0.434 NA NA
PML temozolomide 0.4354604 0.7644487 0.435 NA NA
ESR1 cabozantinib 0.4356954 0.5697555 0.4357 NA NA
ERBB3 ruxolitinib 0.4364470 0.9314078 0.4364 NA NA
MYD88 fulvestrant 0.4383092 0.9110805 0.4383 NA NA
CYP19A1 temozolomide 0.4385706 0.7644487 0.439 NA NA
IDH2 trametinib 0.4390903 0.6262131 0.4391 NA NA
ESR1 sunitinib 0.4399411 0.7545287 0.43994 NA NA
BRCA2 vandetanib 0.4404474 0.8272351 0.4404 NA NA
IDH2 selumetinib 0.4434340 0.7472482 0.44343 NA NA
ALK nilotinib 0.4451309 0.7927775 0.445 NA NA
TSC2 neratinib 0.4493579 0.8334472 0.44936 NA NA
MET temozolomide 0.4496757 0.7644487 0.450 NA NA
ERBB3 pazopanib 0.4547800 0.9261994 0.4548 NA NA
IDH2 dasatinib 0.4572773 0.9092193 0.457 NA NA
ESR1 erlotinib 0.4580536 0.8218115 0.458 NA NA
BRAF erlotinib 0.4592476 0.8218115 0.459 NA NA
G6PD vemurafenib 0.4602214 0.6539778 0.46022 NA NA
ESR1 vemurafenib 0.4616314 0.6539778 0.46163 NA NA
BRAF vandetanib 0.4622785 0.8272351 0.4623 NA NA
PDGFB fluorouracil 0.4641195 0.8677771 0.464 NA NA
MGMT sunitinib 0.4645056 0.7545287 0.46451 NA NA
JAK2 sunitinib 0.4660324 0.7545287 0.46603 NA NA
FLT3 regorafenib 0.4660591 0.8471493 0.4661 NA NA
G6PD fluorouracil 0.4661924 0.8677771 0.466 NA NA
ABL1 gefitinib 0.4664039 0.8346175 0.466 NA NA
AKT1 ruxolitinib 0.4679864 0.9314078 0.4680 NA NA
TPMT ibrutinib 0.4703605 0.8416977 0.4704 NA NA
DNMT3A cabozantinib 0.4741169 0.5970360 0.4741 NA NA
ERBB2 azd 0.4751190 0.6739725 0.475 NA NA
CYP19A1 azd 0.4757453 0.6739725 0.476 NA NA
KIT regorafenib 0.4759780 0.8471493 0.4760 NA NA
CYP19A1 selumetinib 0.4763756 0.7472482 0.47638 NA NA
UGT1A1 fluorouracil 0.4767219 0.8677771 0.477 NA NA
BRCA2 afatinib 0.4780707 0.7569737 0.4781 NA NA
MYD88 trametinib 0.4783281 0.6262131 0.4783 NA NA
BRCA2 crizotinib 0.4789637 0.9896386 0.479 NA NA
BRCA1 neratinib 0.4793342 0.8334472 0.47933 NA NA
BRCA1 imatinib 0.4803511 0.9073299 0.4804 NA NA
G6PD dasatinib 0.4813514 0.9092193 0.481 NA NA
EGFR dabrafenib 0.4817681 0.8681874 0.48177 NA NA
JAK2 trametinib 0.4825043 0.6262131 0.4825 NA NA
G6PD lapatinib 0.4825811 0.7270249 0.4826 NA NA
IDH2 pazopanib 0.4829493 0.9261994 0.4829 NA NA
ABL1 selumetinib 0.4835135 0.7472482 0.48351 NA NA
MGMT belinostat 0.4836415 0.9332615 0.48 NA NA
BRCA2 belinostat 0.4837494 0.9332615 0.48 NA NA
FLT3 belinostat 0.4844586 0.9332615 0.48 NA NA
BRCA1 fluorouracil 0.4849343 0.8677771 0.485 NA NA
DNMT3A temozolomide 0.4872219 0.7776868 0.487 NA NA
ALK regorafenib 0.4873433 0.8471493 0.4873 NA NA
DPYD ruxolitinib 0.4879108 0.9314078 0.4879 NA NA
MET afatinib 0.4898065 0.7569737 0.4898 NA NA
PDGFRA neratinib 0.4902631 0.8334472 0.49026 NA NA
MGMT crizotinib 0.4938707 0.9896386 0.494 NA NA
TSC1 vandetanib 0.4950385 0.8415654 0.4950 NA NA
DPYD lapatinib 0.4952434 0.7270249 0.4952 NA NA
PTCH1 pazopanib 0.4968297 0.9261994 0.4968 NA NA
TSC1 temozolomide 0.5032091 0.7776868 0.503 NA NA
DPYD cyclophosphamide 0.5045811 0.9793614 0.505 NA NA
RET sunitinib 0.5118218 0.7868747 0.51182 NA NA
DPYD trametinib 0.5123073 0.6262131 0.5123 NA NA
UGT1A1 trametinib 0.5123562 0.6262131 0.5124 NA NA
PML lapatinib 0.5131940 0.7270249 0.5132 NA NA
MET neratinib 0.5169277 0.8369306 0.51693 NA NA
RET pazopanib 0.5175936 0.9261994 0.5176 NA NA
TSC2 ruxolitinib 0.5176922 0.9314078 0.5177 NA NA
DPYD dabrafenib 0.5177549 0.8681874 0.51775 NA NA
BRCA1 gefitinib 0.5187110 0.8466777 0.519 NA NA
PDGFRA nilotinib 0.5194411 0.7927775 0.519 NA NA
ABL1 regorafenib 0.5196255 0.8471493 0.5196 NA NA
NPM1 gefitinib 0.5229480 0.8466777 0.523 NA NA
DPYD regorafenib 0.5244529 0.8471493 0.5245 NA NA
BRCA2 olaparib 0.5281161 0.9728161 0.52812 NA NA
DNMT3A nilotinib 0.5289852 0.7927775 0.529 NA NA
AKT1 cyclophosphamide 0.5303222 0.9793614 0.530 NA NA
PDGFRA ruxolitinib 0.5303811 0.9314078 0.5304 NA NA
KIT sunitinib 0.5322976 0.7868747 0.53230 NA NA
FLT3 dasatinib 0.5331194 0.9367075 0.533 NA NA
G6PD cyclophosphamide 0.5360224 0.9793614 0.536 NA NA
ERBB3 crizotinib 0.5361343 0.9896386 0.536 NA NA
MYD88 imatinib 0.5393201 0.9425360 0.5393 NA NA
TSC1 lapatinib 0.5398720 0.7342259 0.5399 NA NA
RET vemurafenib 0.5409337 0.7294806 0.54093 NA NA
BRCA2 dabrafenib 0.5419188 0.8681874 0.54192 NA NA
TSC2 crizotinib 0.5427013 0.9896386 0.543 NA NA
PTCH1 fulvestrant 0.5427377 0.9110805 0.5427 NA NA
BRCA2 cyclophosphamide 0.5442614 0.9793614 0.544 NA NA
NPM1 nilotinib 0.5448245 0.7927775 0.545 NA NA
EGFR erlotinib 0.5472418 0.8891239 0.547 NA NA
EGFR nilotinib 0.5479716 0.7927775 0.548 NA NA
ESR1 regorafenib 0.5481555 0.8471493 0.5482 NA NA
TSC2 erlotinib 0.5491647 0.8891239 0.549 NA NA
ERBB3 selumetinib 0.5514235 0.8151478 0.55142 NA NA
MGMT azd 0.5517454 0.7386733 0.552 NA NA
TSC2 temozolomide 0.5520727 0.8087434 0.552 NA NA
MYD88 neratinib 0.5522299 0.8534462 0.55223 NA NA
JAK2 dabrafenib 0.5524829 0.8681874 0.55248 NA NA
IDH2 ruxolitinib 0.5558028 0.9314078 0.5558 NA NA
PDGFRB fulvestrant 0.5559160 0.9110805 0.5559 NA NA
PDGFB gefitinib 0.5559298 0.8546038 0.556 NA NA
ERBB2 vemurafenib 0.5578381 0.7294806 0.55784 NA NA
TSC2 vandetanib 0.5599331 0.8665251 0.5599 NA NA
ERBB2 vandetanib 0.5606927 0.8665251 0.5607 NA NA
PDGFRB nilotinib 0.5622570 0.7927775 0.562 NA NA
JAK2 ibrutinib 0.5688103 0.9669776 0.5688 NA NA
RET fulvestrant 0.5689282 0.9110805 0.5689 NA NA
MGMT olaparib 0.5699313 0.9728161 0.56993 NA NA
PDGFB olaparib 0.5706892 0.9728161 0.57069 NA NA
FLT3 fulvestrant 0.5722357 0.9110805 0.5722 NA NA
KRAS cyclophosphamide 0.5770737 0.9793614 0.577 NA NA
PML belinostat 0.5778444 0.9332615 0.58 NA NA
PDGFRB gefitinib 0.5781143 0.8546038 0.578 NA NA
DPYD sunitinib 0.5784482 0.8194683 0.57845 NA NA
MYD88 pazopanib 0.5788620 0.9261994 0.5789 NA NA
UGT1A1 afatinib 0.5812670 0.8592643 0.5813 NA NA
ABL1 imatinib 0.5818558 0.9425360 0.5819 NA NA
FLT3 imatinib 0.5821546 0.9425360 0.5822 NA NA
ERBB3 nilotinib 0.5829246 0.7927775 0.583 NA NA
DNMT3A cyclophosphamide 0.5843537 0.9793614 0.584 NA NA
PML cabozantinib 0.5847529 0.6901833 0.5848 NA NA
ROS1 belinostat 0.5877641 0.9332615 0.59 NA NA
AKT1 temozolomide 0.5884213 0.8087434 0.588 NA NA
KIT lapatinib 0.5885275 0.7696129 0.5885 NA NA
ERBB3 fulvestrant 0.5897197 0.9110805 0.5897 NA NA
KIT azd 0.5928965 0.7386733 0.593 NA NA
PDGFRB temozolomide 0.5946643 0.8087434 0.595 NA NA
DPYD cabozantinib 0.5989674 0.6901833 0.5990 NA NA
G6PD ibrutinib 0.6023586 0.9736731 0.6024 NA NA
DNMT3A vemurafenib 0.6061305 0.7525336 0.60613 NA NA
MET azd 0.6072926 0.7386733 0.607 NA NA
G6PD azd 0.6083192 0.7386733 0.608 NA NA
MGMT cyclophosphamide 0.6089169 0.9793614 0.609 NA NA
RET cabozantinib 0.6089852 0.6901833 0.6090 NA NA
PDGFB erlotinib 0.6129494 0.9151456 0.613 NA NA
MYD88 ruxolitinib 0.6181728 0.9314078 0.6182 NA NA
ABL1 nilotinib 0.6182260 0.8084494 0.618 NA NA
CYP19A1 vandetanib 0.6183456 0.8720434 0.6183 NA NA
TSC1 trametinib 0.6186725 0.7291497 0.6187 NA NA
ERBB2 temozolomide 0.6194974 0.8101120 0.619 NA NA
PDGFRB vemurafenib 0.6197336 0.7525336 0.61973 NA NA
TSC1 olaparib 0.6197994 0.9728161 0.61980 NA NA
BRCA1 pazopanib 0.6211989 0.9261994 0.6212 NA NA
PDGFRA dabrafenib 0.6212605 0.9318907 0.62126 NA NA
BRCA1 dasatinib 0.6214626 0.9367075 0.621 NA NA
NPM1 ruxolitinib 0.6223011 0.9314078 0.6223 NA NA
TSC2 lapatinib 0.6258155 0.7879401 0.6258 NA NA
CYP19A1 ruxolitinib 0.6276320 0.9314078 0.6276 NA NA
ERBB2 ruxolitinib 0.6293981 0.9314078 0.6294 NA NA
DNMT3A ruxolitinib 0.6300700 0.9314078 0.6301 NA NA
KIT vandetanib 0.6306055 0.8720434 0.6306 NA NA
AKT1 regorafenib 0.6309258 0.9326729 0.6309 NA NA
AKT1 fluorouracil 0.6312805 0.9561079 0.631 NA NA
RET imatinib 0.6313687 0.9504694 0.6314 NA NA
ESR1 azd 0.6359850 0.7424789 0.636 NA NA
IDH2 crizotinib 0.6363824 0.9896386 0.636 NA NA
TSC1 erlotinib 0.6364596 0.9151456 0.636 NA NA
ERBB3 gefitinib 0.6397928 0.8848141 0.640 NA NA
TSC1 crizotinib 0.6420105 0.9896386 0.642 NA NA
KIT imatinib 0.6429646 0.9504694 0.6430 NA NA
AKT1 pazopanib 0.6440994 0.9261994 0.6441 NA NA
PDGFB selumetinib 0.6445370 0.8818646 0.64454 NA NA
MET erlotinib 0.6459851 0.9151456 0.646 NA NA
BRCA1 selumetinib 0.6484298 0.8818646 0.64843 NA NA
PDGFRA afatinib 0.6518163 0.8631215 0.6518 NA NA
DNMT3A azd 0.6551284 0.7424789 0.655 NA NA
MYD88 fluorouracil 0.6552215 0.9561079 0.655 NA NA
IDH2 cyclophosphamide 0.6554963 0.9793614 0.655 NA NA
UGT1A1 neratinib 0.6590084 0.9509752 0.65901 NA NA
BRCA2 ibrutinib 0.6602730 0.9736731 0.6603 NA NA
PTCH1 belinostat 0.6603291 0.9332615 0.66 NA NA
UGT1A1 lapatinib 0.6619661 0.7879401 0.6620 NA NA
TSC1 pazopanib 0.6622597 0.9261994 0.6623 NA NA
BRAF regorafenib 0.6631522 0.9394657 0.6632 NA NA
AKT1 dasatinib 0.6650170 0.9367075 0.665 NA NA
ALK belinostat 0.6654501 0.9332615 0.67 NA NA
G6PD afatinib 0.6656317 0.8631215 0.6656 NA NA
PDGFRA fulvestrant 0.6659409 0.9110805 0.6659 NA NA
PML dabrafenib 0.6659420 0.9324432 0.66594 NA NA
RET nilotinib 0.6667141 0.8395660 0.667 NA NA
BRCA2 lapatinib 0.6720665 0.7879401 0.6721 NA NA
NPM1 cyclophosphamide 0.6729128 0.9793614 0.673 NA NA
G6PD fulvestrant 0.6741823 0.9110805 0.6742 NA NA
PDGFRB crizotinib 0.6743149 0.9896386 0.674 NA NA
ESR1 fulvestrant 0.6748204 0.9110805 0.6748 NA NA
KIT olaparib 0.6772410 0.9728161 0.67724 NA NA
ESR1 neratinib 0.6776713 0.9509752 0.67767 NA NA
PTCH1 dabrafenib 0.6781405 0.9324432 0.67814 NA NA
ABL1 azd 0.6783940 0.7440450 0.678 NA NA
ABL1 olaparib 0.6795493 0.9728161 0.67955 NA NA
EGFR cabozantinib 0.6800714 0.7458848 0.6801 NA NA
ALK ruxolitinib 0.6822022 0.9664531 0.6822 NA NA
UGT1A1 gefitinib 0.6824391 0.8848141 0.682 NA NA
ABL1 vandetanib 0.6832739 0.8720434 0.6833 NA NA
TSC1 fulvestrant 0.6874271 0.9110805 0.6874 NA NA
PTCH1 fluorouracil 0.6889362 0.9561079 0.689 NA NA
PTCH1 crizotinib 0.6896847 0.9896386 0.690 NA NA
ERBB2 belinostat 0.6914232 0.9332615 0.69 NA NA
CYP19A1 afatinib 0.6916918 0.8631215 0.6917 NA NA
JAK2 olaparib 0.6925765 0.9728161 0.69258 NA NA
PML regorafenib 0.6943881 0.9410069 0.6944 NA NA
ERBB3 afatinib 0.6959010 0.8631215 0.6959 NA NA
EGFR fulvestrant 0.6967086 0.9110805 0.6967 NA NA
BRCA2 dasatinib 0.6990595 0.9367075 0.699 NA NA
TSC2 fluorouracil 0.7001121 0.9561079 0.700 NA NA
G6PD pazopanib 0.7046983 0.9261994 0.7047 NA NA
KRAS belinostat 0.7060390 0.9332615 0.71 NA NA
PDGFRB azd 0.7069233 0.7511060 0.707 NA NA
BRCA2 imatinib 0.7069599 0.9654406 0.7070 NA NA
BRCA1 vandetanib 0.7073522 0.8720434 0.7074 NA NA
UGT1A1 erlotinib 0.7111266 0.9491271 0.711 NA NA
FLT3 gefitinib 0.7182420 0.8848141 0.718 NA NA
TSC1 belinostat 0.7219457 0.9332615 0.72 NA NA
ROS1 vandetanib 0.7251785 0.8720434 0.7252 NA NA
FLT3 erlotinib 0.7258031 0.9491271 0.726 NA NA
PDGFB vandetanib 0.7261157 0.8720434 0.7261 NA NA
MET fluorouracil 0.7280738 0.9561079 0.728 NA NA
FLT3 trametinib 0.7282232 0.8286678 0.7282 NA NA
ROS1 ibrutinib 0.7285250 0.9736731 0.7285 NA NA
PML cyclophosphamide 0.7303617 0.9793614 0.730 NA NA
TSC1 ruxolitinib 0.7316331 0.9851643 0.7316 NA NA
AKT1 imatinib 0.7320299 0.9654406 0.7320 NA NA
TSC2 gefitinib 0.7322403 0.8848141 0.732 NA NA
PTCH1 afatinib 0.7324281 0.8631215 0.7324 NA NA
ERBB3 neratinib 0.7332982 0.9509752 0.73330 NA NA
FLT3 selumetinib 0.7342484 0.8927666 0.73425 NA NA
ERBB3 fluorouracil 0.7345804 0.9561079 0.735 NA NA
ERBB2 nilotinib 0.7346282 0.8710739 0.735 NA NA
TSC1 afatinib 0.7361919 0.8631215 0.7362 NA NA
PDGFB sunitinib 0.7395816 0.9565998 0.73958 NA NA
PDGFRA crizotinib 0.7420103 0.9896386 0.742 NA NA
UGT1A1 nilotinib 0.7429748 0.8710739 0.743 NA NA
MGMT selumetinib 0.7434177 0.8927666 0.74342 NA NA
PML vandetanib 0.7438018 0.8720434 0.7438 NA NA
G6PD olaparib 0.7455081 0.9728161 0.74551 NA NA
BRCA2 gefitinib 0.7477970 0.8848141 0.748 NA NA
TSC2 imatinib 0.7480994 0.9654406 0.7481 NA NA
JAK2 selumetinib 0.7485902 0.8927666 0.74859 NA NA
DPYD crizotinib 0.7496551 0.9896386 0.750 NA NA
MYD88 crizotinib 0.7508273 0.9896386 0.751 NA NA
PDGFRB dasatinib 0.7535038 0.9367075 0.754 NA NA
PTCH1 gefitinib 0.7546944 0.8848141 0.755 NA NA
PDGFRB dabrafenib 0.7547325 0.9480461 0.75473 NA NA
NPM1 olaparib 0.7587867 0.9728161 0.75879 NA NA
G6PD temozolomide 0.7612249 0.9585795 0.761 NA NA
PDGFRB lapatinib 0.7618885 0.8634737 0.7619 NA NA
BRCA2 regorafenib 0.7635037 0.9410069 0.7635 NA NA
DPYD imatinib 0.7666734 0.9654406 0.7667 NA NA
TSC2 olaparib 0.7680842 0.9728161 0.76808 NA NA
EGFR sunitinib 0.7681397 0.9565998 0.76814 NA NA
DPYD fluorouracil 0.7693491 0.9561079 0.769 NA NA
FLT3 sunitinib 0.7714998 0.9565998 0.77150 NA NA
MYD88 dasatinib 0.7715129 0.9367075 0.772 NA NA
EGFR fluorouracil 0.7720863 0.9561079 0.772 NA NA
ERBB2 ibrutinib 0.7726805 0.9736731 0.7727 NA NA
PDGFRB vandetanib 0.7728525 0.8735853 0.7729 NA NA
TSC2 belinostat 0.7739206 0.9332615 0.77 NA NA
UGT1A1 vemurafenib 0.7774617 0.9065616 0.77746 NA NA
ESR1 dasatinib 0.7776986 0.9367075 0.778 NA NA
KIT dasatinib 0.7790279 0.9367075 0.779 NA NA
MYD88 regorafenib 0.7798439 0.9410069 0.7798 NA NA
ABL1 dasatinib 0.7801253 0.9367075 0.780 NA NA
ROS1 dasatinib 0.7804679 0.9367075 0.780 NA NA
BRCA1 erlotinib 0.7809136 0.9494085 0.781 NA NA
MYD88 selumetinib 0.7817427 0.8927666 0.78174 NA NA
RET ibrutinib 0.7824502 0.9736731 0.7825 NA NA
EGFR belinostat 0.7834615 0.9332615 0.78 NA NA
CYP19A1 neratinib 0.7845031 0.9509752 0.78450 NA NA
MET belinostat 0.7848683 0.9332615 0.78 NA NA
PDGFRB cyclophosphamide 0.7850095 0.9793614 0.785 NA NA
MGMT cabozantinib 0.7880782 0.8373331 0.7881 NA NA
BRCA1 trametinib 0.7902084 0.8422588 0.7902 NA NA
ALK pazopanib 0.7907566 0.9261994 0.7908 NA NA
NPM1 trametinib 0.7912128 0.8422588 0.7912 NA NA
MYD88 sunitinib 0.7948466 0.9565998 0.79485 NA NA
FLT3 fluorouracil 0.7949588 0.9561079 0.795 NA NA
BRCA2 fulvestrant 0.7961724 0.9582702 0.7962 NA NA
ESR1 vandetanib 0.7965042 0.8735853 0.7965 NA NA
IDH2 belinostat 0.7972643 0.9332615 0.80 NA NA
NPM1 regorafenib 0.8002281 0.9410069 0.8002 NA NA
ROS1 dabrafenib 0.8003190 0.9480461 0.80032 NA NA
CYP19A1 erlotinib 0.8021724 0.9494085 0.802 NA NA
TSC2 dabrafenib 0.8039511 0.9480461 0.80395 NA NA
ERBB2 pazopanib 0.8041509 0.9261994 0.8042 NA NA
EGFR imatinib 0.8043300 0.9690754 0.8043 NA NA
ERBB3 vemurafenib 0.8058873 0.9065616 0.80589 NA NA
IDH2 gefitinib 0.8069304 0.9145211 0.807 NA NA
AKT1 fulvestrant 0.8069482 0.9582702 0.8069 NA NA
TPMT temozolomide 0.8089661 0.9600068 0.809 NA NA
NPM1 dasatinib 0.8123866 0.9367075 0.812 NA NA
BRCA1 olaparib 0.8126710 0.9728161 0.81267 NA NA
FLT3 afatinib 0.8136811 0.9017076 0.8137 NA NA
TSC2 pazopanib 0.8156658 0.9261994 0.8157 NA NA
EGFR selumetinib 0.8175946 0.8927666 0.81759 NA NA
KRAS fluorouracil 0.8178870 0.9561079 0.818 NA NA
DNMT3A dabrafenib 0.8196290 0.9480461 0.81963 NA NA
KRAS erlotinib 0.8197849 0.9494085 0.820 NA NA
JAK2 ruxolitinib 0.8206972 0.9851643 0.8207 NA NA
ABL1 neratinib 0.8209775 0.9509752 0.82098 NA NA
JAK2 pazopanib 0.8212255 0.9261994 0.8212 NA NA
ESR1 afatinib 0.8221452 0.9017076 0.8221 NA NA
AKT1 selumetinib 0.8255265 0.8927666 0.82553 NA NA
ERBB3 dasatinib 0.8265066 0.9367075 0.827 NA NA
ERBB3 regorafenib 0.8265906 0.9410069 0.8266 NA NA
G6PD belinostat 0.8270727 0.9332615 0.83 NA NA
BRCA2 sunitinib 0.8280670 0.9565998 0.82807 NA NA
TSC2 regorafenib 0.8307456 0.9410069 0.8307 NA NA
ABL1 dabrafenib 0.8331314 0.9480461 0.83313 NA NA
JAK2 imatinib 0.8347277 0.9690754 0.8347 NA NA
ABL1 pazopanib 0.8358559 0.9261994 0.8359 NA NA
TPMT nilotinib 0.8378567 0.9337740 0.838 NA NA
BRCA2 ruxolitinib 0.8386749 0.9851643 0.8387 NA NA
TPMT fulvestrant 0.8400737 0.9582702 0.8401 NA NA
KIT belinostat 0.8435673 0.9332615 0.84 NA NA
ABL1 sunitinib 0.8440587 0.9565998 0.84406 NA NA
EGFR cyclophosphamide 0.8442118 0.9793614 0.844 NA NA
TPMT belinostat 0.8445547 0.9332615 0.84 NA NA
PDGFRB erlotinib 0.8448409 0.9494085 0.845 NA NA
PML azd 0.8448624 0.8704643 0.845 NA NA
PDGFB fulvestrant 0.8455325 0.9582702 0.8455 NA NA
DNMT3A belinostat 0.8463546 0.9332615 0.85 NA NA
ALK cabozantinib 0.8474970 0.8731787 0.8475 NA NA
JAK2 belinostat 0.8481782 0.9332615 0.85 NA NA
ESR1 ruxolitinib 0.8489985 0.9851643 0.8490 NA NA
MGMT pazopanib 0.8490601 0.9261994 0.8491 NA NA
PDGFB afatinib 0.8499921 0.9031166 0.8500 NA NA
BRCA1 ibrutinib 0.8510774 0.9736731 0.8511 NA NA
IDH2 nilotinib 0.8513822 0.9337740 0.851 NA NA
CYP19A1 pazopanib 0.8533966 0.9261994 0.8534 NA NA
PDGFRA imatinib 0.8550665 0.9690754 0.8551 NA NA
PDGFB crizotinib 0.8558808 0.9896386 0.856 NA NA
PTCH1 neratinib 0.8565156 0.9509752 0.85652 NA NA
G6PD neratinib 0.8580742 0.9509752 0.85807 NA NA
ERBB3 temozolomide 0.8609254 0.9600068 0.861 NA NA
FLT3 neratinib 0.8622113 0.9509752 0.86221 NA NA
ERBB3 trametinib 0.8637892 0.8907826 0.8638 NA NA
ROS1 selumetinib 0.8640047 0.8927666 0.86400 NA NA
MET ruxolitinib 0.8650705 0.9851643 0.8651 NA NA
TSC2 selumetinib 0.8665087 0.8927666 0.86651 NA NA
BRCA1 cyclophosphamide 0.8713387 0.9793614 0.871 NA NA
ALK cyclophosphamide 0.8716671 0.9793614 0.872 NA NA
ERBB2 regorafenib 0.8725903 0.9410069 0.8726 NA NA
EGFR temozolomide 0.8736252 0.9600068 0.874 NA NA
PML ibrutinib 0.8740839 0.9736731 0.8741 NA NA
MET ibrutinib 0.8773082 0.9736731 0.8773 NA NA
G6PD erlotinib 0.8787499 0.9494085 0.879 NA NA
PDGFB cabozantinib 0.8796954 0.8796954 0.8797 NA NA
BRCA1 ruxolitinib 0.8813093 0.9851643 0.8813 NA NA
ERBB3 olaparib 0.8819557 0.9728161 0.88196 NA NA
NPM1 vemurafenib 0.8824014 0.9065616 0.88240 NA NA
ESR1 pazopanib 0.8825243 0.9261994 0.8825 NA NA
ABL1 vemurafenib 0.8828642 0.9065616 0.88286 NA NA
KIT vemurafenib 0.8838594 0.9065616 0.88386 NA NA
BRAF crizotinib 0.8847422 0.9896386 0.885 NA NA
MGMT regorafenib 0.8856536 0.9410069 0.8857 NA NA
FLT3 azd 0.8863568 0.8863568 0.886 NA NA
TSC2 fulvestrant 0.8866945 0.9688694 0.8867 NA NA
RET temozolomide 0.8870447 0.9600068 0.887 NA NA
EGFR gefitinib 0.8875800 0.9734748 0.888 NA NA
MET dasatinib 0.8890135 0.9662257 0.889 NA NA
PML neratinib 0.8916365 0.9509752 0.89164 NA NA
ABL1 erlotinib 0.8935609 0.9494085 0.894 NA NA
KIT nilotinib 0.8943017 0.9501956 0.894 NA NA
MGMT ibrutinib 0.8947146 0.9736731 0.8947 NA NA
JAK2 lapatinib 0.8953005 0.9427410 0.8953 NA NA
MET imatinib 0.8955374 0.9790505 0.8955 NA NA
ERBB2 lapatinib 0.8966122 0.9427410 0.8966 NA NA
BRCA2 fluorouracil 0.8966696 0.9561079 0.897 NA NA
MYD88 belinostat 0.8969609 0.9332615 0.90 NA NA
CYP19A1 crizotinib 0.8996262 0.9896386 0.900 NA NA
IDH2 sunitinib 0.9001651 0.9857115 0.90017 NA NA
FLT3 cyclophosphamide 0.9009165 0.9793614 0.901 NA NA
NPM1 belinostat 0.9011013 0.9332615 0.90 NA NA
IDH2 temozolomide 0.9035358 0.9600068 0.904 NA NA
TPMT vemurafenib 0.9065616 0.9065616 0.90656 NA NA
ESR1 crizotinib 0.9085657 0.9896386 0.909 NA NA
JAK2 dasatinib 0.9093889 0.9662257 0.909 NA NA
ESR1 cyclophosphamide 0.9094059 0.9793614 0.909 NA NA
RET olaparib 0.9112727 0.9728161 0.91127 NA NA
DNMT3A fulvestrant 0.9118771 0.9688694 0.9119 NA NA
PTCH1 vandetanib 0.9121958 0.9305740 0.9122 NA NA
JAK2 fluorouracil 0.9122887 0.9561079 0.912 NA NA
PTCH1 ruxolitinib 0.9145576 0.9851643 0.9146 NA NA
PDGFB lapatinib 0.9150133 0.9427410 0.9150 NA NA
AKT1 dabrafenib 0.9165171 0.9896121 0.91652 NA NA
DPYD pazopanib 0.9171346 0.9261994 0.9171 NA NA
ROS1 fluorouracil 0.9179969 0.9561079 0.918 NA NA
PDGFRA olaparib 0.9187781 0.9728161 0.91878 NA NA
EGFR neratinib 0.9192244 0.9509752 0.91922 NA NA
PTCH1 cyclophosphamide 0.9194906 0.9793614 0.919 NA NA
TSC2 ibrutinib 0.9206937 0.9736731 0.9207 NA NA
PDGFB cyclophosphamide 0.9213531 0.9793614 0.921 NA NA
ESR1 selumetinib 0.9216779 0.9216779 0.92168 NA NA
MYD88 cyclophosphamide 0.9220687 0.9793614 0.922 NA NA
CYP19A1 trametinib 0.9221480 0.9221480 0.9221 NA NA
UGT1A1 belinostat 0.9223455 0.9332615 0.92 NA NA
JAK2 neratinib 0.9230054 0.9509752 0.92301 NA NA
ESR1 belinostat 0.9247735 0.9332615 0.92 NA NA
PDGFRA belinostat 0.9258624 0.9332615 0.93 NA NA
EGFR pazopanib 0.9261994 0.9261994 0.9262 NA NA
BRCA2 nilotinib 0.9267694 0.9548533 0.927 NA NA
UGT1A1 vandetanib 0.9276534 0.9305740 0.9277 NA NA
FLT3 vandetanib 0.9305740 0.9305740 0.9306 NA NA
AKT1 belinostat 0.9332615 0.9332615 0.93 NA NA
BRCA1 afatinib 0.9350732 0.9634087 0.9351 NA NA
ERBB3 ibrutinib 0.9351931 0.9736731 0.9352 NA NA
IDH2 ibrutinib 0.9361769 0.9736731 0.9362 NA NA
EGFR crizotinib 0.9363173 0.9896386 0.936 NA NA
IDH2 fluorouracil 0.9380996 0.9561079 0.938 NA NA
MYD88 olaparib 0.9407553 0.9728161 0.94076 NA NA
ERBB2 sunitinib 0.9415781 0.9857115 0.94158 NA NA
FLT3 ruxolitinib 0.9427873 0.9851643 0.9428 NA NA
IDH2 regorafenib 0.9449361 0.9661684 0.9449 NA NA
CYP19A1 gefitinib 0.9492185 0.9871832 0.949 NA NA
ROS1 neratinib 0.9510033 0.9510033 0.95100 NA NA
NPM1 imatinib 0.9542718 0.9790505 0.9543 NA NA
KIT fluorouracil 0.9561079 0.9561079 0.956 NA NA
G6PD ruxolitinib 0.9561889 0.9851643 0.9562 NA NA
PDGFRB olaparib 0.9563573 0.9728161 0.95636 NA NA
ABL1 lapatinib 0.9564785 0.9564785 0.9565 NA NA
ALK sunitinib 0.9567200 0.9857115 0.95672 NA NA
PDGFB temozolomide 0.9628530 0.9895199 0.963 NA NA
MYD88 erlotinib 0.9646182 0.9832344 0.965 NA NA
PTCH1 ibrutinib 0.9649617 0.9736731 0.9650 NA NA
TSC1 cyclophosphamide 0.9655074 0.9793614 0.966 NA NA
ESR1 dabrafenib 0.9659108 0.9896121 0.96591 NA NA
DNMT3A regorafenib 0.9661684 0.9661684 0.9662 NA NA
BRCA1 dabrafenib 0.9666621 0.9896121 0.96666 NA NA
KIT fulvestrant 0.9667212 0.9765345 0.9667 NA NA
ERBB2 olaparib 0.9669476 0.9728161 0.96695 NA NA
AKT1 nilotinib 0.9672155 0.9672155 0.967 NA NA
ROS1 imatinib 0.9684583 0.9790505 0.9685 NA NA
ERBB2 crizotinib 0.9689644 0.9896386 0.969 NA NA
TSC1 gefitinib 0.9708568 0.9871832 0.971 NA NA
DPYD olaparib 0.9728161 0.9728161 0.97282 NA NA
DPYD ibrutinib 0.9736731 0.9736731 0.9737 NA NA
G6PD crizotinib 0.9756109 0.9896386 0.976 NA NA
UGT1A1 fulvestrant 0.9765345 0.9765345 0.9765 NA NA
DPYD dasatinib 0.9779403 0.9980158 0.978 NA NA
G6PD imatinib 0.9790505 0.9790505 0.9791 NA NA
PDGFRA cyclophosphamide 0.9793614 0.9793614 0.979 NA NA
ERBB3 erlotinib 0.9832344 0.9832344 0.983 NA NA
BRAF gefitinib 0.9871832 0.9871832 0.987 NA NA
BRCA1 crizotinib 0.9886434 0.9896386 0.989 NA NA
KIT temozolomide 0.9895199 0.9895199 0.990 NA NA
RET dabrafenib 0.9896121 0.9896121 0.98961 NA NA
FLT3 crizotinib 0.9896386 0.9896386 0.990 NA NA
BRAF sunitinib 0.9897961 0.9897961 0.98980 NA NA
NPM1 afatinib 0.9954026 0.9954026 0.9954 NA NA
KIT ruxolitinib 0.9977843 0.9977843 0.9978 NA NA
UGT1A1 dasatinib 0.9980158 0.9980158 0.998 NA NA

5.5 GDSC: G2P gene and drug

gdsc_signif_g2p <- compare_means(AUC ~ Mutation_Status_Nonsilent, group.by = "Drug_Gene", data = gdsc_data_g2p, method = "wilcox.test", p.adjust.method = "BH")
gdsc_signif_g2p <- adj_signif(gdsc_signif_g2p)
gdsc_signif_g2p <- gdsc_signif_g2p[order(gdsc_signif_g2p$p),]
saveRDS(gdsc_signif_g2p, "./data_munging/rds/gdsc_signif_g2p_gene.rds")

gdsc_signif_g2p_ByDrug <- lapply(unique(gdsc_data_g2p_genesfilt$Drug), WilcoxonByDrug, dataset = gdsc_data_g2p_genesfilt, data_name = "gdsc")
names(gdsc_signif_g2p_ByDrug) <- unique(gdsc_data_g2p_genesfilt$Drug)
saveRDS(gdsc_signif_g2p_ByDrug, "./data_munging/rds/gdsc_signif_g2p_ByDrug.rds")
gdsc_signif_g2p <- readRDS("./data_munging/rds/gdsc_signif_g2p_gene.rds")

knitr::kable(gdsc_signif_g2p[, c("Drug_Gene", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "GDSC: Wilcoxon test results for Level A point mutations, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
GDSC: Wilcoxon test results for Level A point mutations, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Drug_Gene p p.adj p.format p.signif p.signif.adj
dabrafenib_BRAF 0.0001234 0.0034674 0.00012 *** **
trametinib_BRAF 0.0001576 0.0034674 0.00016 *** **
afatinib_ERBB2 0.0171962 0.2522113 0.01720
NA
ponatinib_ABL1 0.0614896 0.6763861 0.06149 NA NA
lapatinib_ERBB2 0.1230985 0.8133548 0.12310 NA NA
erlotinib_MET 0.1281272 0.8133548 0.12813 NA NA
crizotinib_MET 0.1305419 0.8133548 0.13054 NA NA
gefitinib_EGFR 0.1651667 0.8133548 0.16517 NA NA
afatinib_KRAS 0.1838668 0.8133548 0.18387 NA NA
alectinib_ROS1 0.2335276 0.8133548 0.23353 NA NA
dabrafenib_G6PD 0.2443393 0.8133548 0.24434 NA NA
imatinib_PDGFB 0.2502352 0.8133548 0.25024 NA NA
rucaparib_BRCA2 0.3140675 0.8133548 0.31407 NA NA
imatinib_PDGFRA 0.3143676 0.8133548 0.31437 NA NA
crizotinib_ALK 0.3700920 0.8133548 0.37009 NA NA
gefitinib_MET 0.3977576 0.8133548 0.39776 NA NA
pazopanib_UGT1A1 0.3984406 0.8133548 0.39844 NA NA
belinostat_UGT1A1 0.4041103 0.8133548 0.40411 NA NA
tretinoin_PML 0.4081601 0.8133548 0.40816 NA NA
dasatinib_ABL1 0.4250747 0.8133548 0.42507 NA NA
afatinib_EGFR 0.4594260 0.8133548 0.45943 NA NA
imatinib_ABL1 0.4679914 0.8133548 0.46799 NA NA
imatinib_PDGFRB 0.4699331 0.8133548 0.46993 NA NA
erlotinib_KRAS 0.4886809 0.8133548 0.48868 NA NA
nilotinib_UGT1A1 0.4918297 0.8133548 0.49183 NA NA
sunitinib_PDGFRA 0.4989956 0.8133548 0.49900 NA NA
cabozantinib_RET 0.4991041 0.8133548 0.49910 NA NA
afatinib_ERBB3 0.5614904 0.8250718 0.56149 NA NA
rucaparib_BRCA1 0.5768966 0.8250718 0.57690 NA NA
gefitinib_KRAS 0.6235545 0.8250718 0.62355 NA NA
lapatinib_KRAS 0.6456344 0.8250718 0.64563 NA NA
midostaurin_FLT3 0.6526579 0.8250718 0.65266 NA NA
sunitinib_KIT 0.6583214 0.8250718 0.65832 NA NA
temozolomide_MGMT 0.6634570 0.8250718 0.66346 NA NA
alectinib_ALK 0.6916060 0.8250718 0.69161 NA NA
docetaxel_ERBB2 0.7052502 0.8250718 0.70525 NA NA
palbociclib_ERBB2 0.7083268 0.8250718 0.70833 NA NA
vismodegib_PTCH1 0.7198241 0.8250718 0.71982 NA NA
nilotinib_ABL1 0.7313136 0.8250718 0.73131 NA NA
imatinib_KIT 0.7505218 0.8255740 0.75052 NA NA
bosutinib monohydrate_ABL1 0.7708229 0.8272245 0.77082 NA NA
ruxolitinib_JAK2 0.8523436 0.8929314 0.85234 NA NA
crizotinib_ROS1 0.9055984 0.9218958 0.90560 NA NA
erlotinib_EGFR 0.9218958 0.9218958 0.92190 NA NA 
gdsc_signif_g2p_ByDrug <- readRDS("./data_munging/rds/gdsc_signif_g2p_ByDrug.rds")
gdsc_signif_g2p_ByDrug_all <- rbindlist(gdsc_signif_g2p_ByDrug, use.names = TRUE)
gdsc_signif_g2p_ByDrug_all <- gdsc_signif_g2p_ByDrug_all[order(gdsc_signif_g2p_ByDrug_all$p),]

knitr::kable(gdsc_signif_g2p_ByDrug_all[, c("Hugo_Symbol", "Drug", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "GDSC: by-drug Wilcoxon test results for Level A point mutations, (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
GDSC: by-drug Wilcoxon test results for Level A point mutations, (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol Drug p p.adj p.format p.signif p.signif.adj
KRAS trametinib 0.0000000 0.0000003 8.5e-09 **** ****
BRAF dabrafenib 0.0001234 0.0041947 0.00012 *** **
BRAF trametinib 0.0001576 0.0026794 0.00016 *** **
PDGFRB cabozantinib 0.0005036 0.0171218 0.0005 ***
KRAS palbociclib 0.0017081 0.0580770 0.0017 ** NA
PDGFRB tretinoin 0.0032063 0.1090147 0.0032 ** NA
ROS1 cabozantinib 0.0037056 0.0629948 0.0037 ** NA
KRAS pazopanib 0.0043870 0.1308570 0.0044 ** NA
BRCA2 midostaurin 0.0058907 0.2002841 0.0059 ** NA
TSC1 temozolomide 0.0073262 0.2490920 0.0073 ** NA
ABL1 pazopanib 0.0076975 0.1308570 0.0077 ** NA
ALK palbociclib 0.0089754 0.1525816 0.0090 ** NA
ROS1 trametinib 0.0160528 0.1819317 0.01605
NA
DNMT3A temozolomide 0.0163008 0.2771142 0.0163
NA
BRCA1 tretinoin 0.0170421 0.2897149 0.0170
NA
ERBB2 afatinib 0.0171962 0.5609625 0.017
NA
BRAF bosutinib monohydrate 0.0181741 0.3173957 0.018
NA
KIT bosutinib monohydrate 0.0186703 0.3173957 0.019
NA
ABL1 gefitinib 0.0196537 0.6682274 0.020
NA
BRCA2 erlotinib 0.0208851 0.6683217 0.021
NA
DPYD lapatinib 0.0212882 0.6812223 0.021
NA
TPMT cabozantinib 0.0218657 0.2413818 0.0219
NA
MGMT docetaxel 0.0241785 0.6845085 0.024
NA
UGT1A1 trametinib 0.0274916 0.2336787 0.02749
NA
KRAS dabrafenib 0.0278924 0.3904427 0.02789
NA
FLT3 cabozantinib 0.0283979 0.2413818 0.0284
NA
TSC1 sunitinib 0.0300384 0.5453213 0.030
NA
ERBB3 ponatinib 0.0344904 0.9972326 0.034
NA
NPM1 tretinoin 0.0355459 0.3525943 0.0355
NA
JAK2 dabrafenib 0.0362699 0.3904427 0.03627
NA
FLT3 crizotinib 0.0371049 0.3575404 0.037
NA
UGT1A1 afatinib 0.0384529 0.5609625 0.038
NA
UGT1A1 midostaurin 0.0385592 0.6011299 0.0386
NA
ERBB3 temozolomide 0.0387034 0.4275924 0.0387
NA
ESR1 imatinib 0.0394429 0.7846892 0.039
NA
TSC1 crizotinib 0.0396633 0.3575404 0.040
NA
BRCA2 crizotinib 0.0408902 0.3575404 0.041
NA
TSC1 tretinoin 0.0414817 0.3525943 0.0415
NA
ABL1 cabozantinib 0.0433477 0.2947646 0.0433
NA
DNMT3A sunitinib 0.0480660 0.5453213 0.048
NA
TPMT temozolomide 0.0503050 0.4275924 0.0503 NA NA
TSC1 rucaparib 0.0503265 0.8376438 0.050 NA NA
NPM1 dabrafenib 0.0506987 0.3904427 0.05070 NA NA
ABL1 afatinib 0.0507264 0.5609625 0.051 NA NA
KIT nilotinib 0.0509383 0.7074000 0.051 NA NA
RET sunitinib 0.0511239 0.5453213 0.051 NA NA
ERBB3 dabrafenib 0.0574180 0.3904427 0.05742 NA NA
TSC1 bosutinib monohydrate 0.0576889 0.6538081 0.058 NA NA
DNMT3A crizotinib 0.0577322 0.3575404 0.058 NA NA
PML crizotinib 0.0605225 0.3575404 0.061 NA NA
RET alectinib 0.0609957 0.6998836 0.061 NA NA
ABL1 ponatinib 0.0614896 0.9972326 0.061 NA NA
PDGFRA alectinib 0.0638924 0.6998836 0.064 NA NA
PTCH1 cabozantinib 0.0650780 0.3687756 0.0651 NA NA
PDGFB ruxolitinib 0.0653257 0.7847293 0.065 NA NA
TPMT belinostat 0.0656977 0.6734824 0.066 NA NA
ABL1 ruxolitinib 0.0661262 0.7847293 0.066 NA NA
PDGFRB belinostat 0.0666829 0.6734824 0.067 NA NA
CYP19A1 ruxolitinib 0.0692408 0.7847293 0.069 NA NA
DNMT3A docetaxel 0.0692678 0.6845085 0.069 NA NA
BRCA2 lapatinib 0.0716745 0.7620951 0.072 NA NA
KIT trametinib 0.0733706 0.4303556 0.07337 NA NA
TPMT dabrafenib 0.0737570 0.4179561 0.07376 NA NA
BRAF belinostat 0.0744959 0.6734824 0.074 NA NA
PDGFRB dasatinib 0.0763115 0.9447103 0.076 NA NA
DPYD crizotinib 0.0771266 0.3575404 0.077 NA NA
TSC1 nilotinib 0.0779898 0.7074000 0.078 NA NA
ERBB2 crizotinib 0.0782120 0.3575404 0.078 NA NA
ALK gefitinib 0.0794375 0.7564295 0.079 NA NA
FLT3 erlotinib 0.0848216 0.6714960 0.085 NA NA
AKT1 nilotinib 0.0856959 0.7074000 0.086 NA NA
PDGFRB midostaurin 0.0887296 0.6011299 0.0887 NA NA
JAK2 trametinib 0.0900127 0.4303556 0.09001 NA NA
UGT1A1 tretinoin 0.0911112 0.3862972 0.0911 NA NA
BRCA2 tretinoin 0.0916313 0.3862972 0.0916 NA NA
ABL1 docetaxel 0.0918631 0.6845085 0.092 NA NA
BRCA2 dasatinib 0.0931223 0.9447103 0.093 NA NA
IDH2 cabozantinib 0.0934140 0.4267811 0.0934 NA NA
TPMT alectinib 0.0935866 0.6998836 0.094 NA NA
ROS1 erlotinib 0.0943399 0.6714960 0.094 NA NA
MYD88 ruxolitinib 0.0954788 0.8115699 0.095 NA NA
NPM1 dasatinib 0.0957018 0.9447103 0.096 NA NA
TPMT tretinoin 0.0957413 0.3862972 0.0957 NA NA
MET lapatinib 0.0972319 0.7620951 0.097 NA NA
DNMT3A rucaparib 0.0975186 0.8376438 0.098 NA NA
RET palbociclib 0.0983382 0.7307299 0.0983 NA NA
ERBB2 gefitinib 0.0989296 0.7564295 0.099 NA NA
FLT3 imatinib 0.0990080 0.7846892 0.099 NA NA
JAK2 cabozantinib 0.1004191 0.4267811 0.1004 NA NA
BRCA2 palbociclib 0.1027690 0.7307299 0.1028 NA NA
MYD88 trametinib 0.1042757 0.4303556 0.10428 NA NA
PML midostaurin 0.1046555 0.6011299 0.1047 NA NA
TSC2 tretinoin 0.1061419 0.3862972 0.1061 NA NA
ABL1 trametinib 0.1069974 0.4303556 0.10700 NA NA
PDGFRA gefitinib 0.1077698 0.7564295 0.108 NA NA
CYP19A1 imatinib 0.1086620 0.7846892 0.109 NA NA
ERBB3 midostaurin 0.1104412 0.6011299 0.1104 NA NA
TSC2 dabrafenib 0.1118016 0.4630570 0.11180 NA NA
BRAF palbociclib 0.1131136 0.7307299 0.1131 NA NA
ROS1 tretinoin 0.1136424 0.3862972 0.1136 NA NA
ROS1 ponatinib 0.1143242 0.9972326 0.114 NA NA
ESR1 temozolomide 0.1154649 0.6370529 0.1155 NA NA
G6PD bosutinib monohydrate 0.1162560 0.8517410 0.116 NA NA
BRCA2 imatinib 0.1174800 0.7846892 0.117 NA NA
PDGFRA tretinoin 0.1216752 0.3862972 0.1217 NA NA
MGMT afatinib 0.1228036 0.5609625 0.123 NA NA
ERBB2 lapatinib 0.1230985 0.7620951 0.123 NA NA
TPMT ruxolitinib 0.1231531 0.8374409 0.123 NA NA
ERBB2 midostaurin 0.1231748 0.6011299 0.1232 NA NA
IDH2 belinostat 0.1232427 0.6734824 0.123 NA NA
KIT tretinoin 0.1249785 0.3862972 0.1250 NA NA
UGT1A1 bosutinib monohydrate 0.1252560 0.8517410 0.125 NA NA
ALK dabrafenib 0.1254923 0.4630570 0.12549 NA NA
MYD88 docetaxel 0.1258052 0.6845085 0.126 NA NA
MGMT gefitinib 0.1263998 0.7564295 0.126 NA NA
PTCH1 midostaurin 0.1266216 0.6011299 0.1266 NA NA
MET erlotinib 0.1281272 0.6714960 0.128 NA NA
MGMT erlotinib 0.1288089 0.6714960 0.129 NA NA
MET crizotinib 0.1305419 0.5221677 0.131 NA NA
ERBB2 temozolomide 0.1311049 0.6370529 0.1311 NA NA
IDH2 temozolomide 0.1311580 0.6370529 0.1312 NA NA
TSC2 nilotinib 0.1328358 0.7074000 0.133 NA NA
PDGFRA afatinib 0.1332170 0.5609625 0.133 NA NA
PTCH1 gefitinib 0.1346354 0.7564295 0.135 NA NA
PDGFRA nilotinib 0.1346957 0.7074000 0.135 NA NA
MET belinostat 0.1348014 0.6734824 0.135 NA NA
EGFR dabrafenib 0.1350723 0.4630570 0.13507 NA NA
PDGFRA trametinib 0.1350935 0.4303556 0.13509 NA NA
MET dabrafenib 0.1361932 0.4630570 0.13619 NA NA
JAK2 vismodegib 0.1385135 0.8104430 0.14 NA NA
ALK trametinib 0.1385711 0.4303556 0.13857 NA NA
TSC2 trametinib 0.1392327 0.4303556 0.13923 NA NA
PDGFRA cabozantinib 0.1407290 0.4442478 0.1407 NA NA
UGT1A1 lapatinib 0.1409522 0.7620951 0.141 NA NA
BRAF rucaparib 0.1413053 0.8376438 0.141 NA NA
AKT1 midostaurin 0.1419954 0.6011299 0.1420 NA NA
ALK lapatinib 0.1428928 0.7620951 0.143 NA NA
KIT cabozantinib 0.1434514 0.4442478 0.1435 NA NA
KRAS cabozantinib 0.1437272 0.4442478 0.1437 NA NA
PDGFB erlotinib 0.1449924 0.6714960 0.145 NA NA
ERBB3 imatinib 0.1453832 0.7846892 0.145 NA NA
UGT1A1 erlotinib 0.1468898 0.6714960 0.147 NA NA
RET docetaxel 0.1477981 0.6845085 0.148 NA NA
G6PD alectinib 0.1491983 0.6998836 0.149 NA NA
NPM1 belinostat 0.1498150 0.6734824 0.150 NA NA
IDH2 rucaparib 0.1502920 0.8376438 0.150 NA NA
MET imatinib 0.1511805 0.7846892 0.151 NA NA
AKT1 bosutinib monohydrate 0.1516806 0.8595235 0.152 NA NA
BRAF alectinib 0.1526449 0.6998836 0.153 NA NA
FLT3 nilotinib 0.1535908 0.7074000 0.154 NA NA
TSC2 afatinib 0.1594651 0.5609625 0.159 NA NA
BRAF vismodegib 0.1624273 0.8104430 0.16 NA NA
IDH2 afatinib 0.1625545 0.5609625 0.163 NA NA
TSC2 temozolomide 0.1628876 0.6854745 0.1629 NA NA
EGFR gefitinib 0.1651667 0.7564295 0.165 NA NA
BRCA1 midostaurin 0.1683042 0.6011299 0.1683 NA NA
ROS1 docetaxel 0.1704250 0.6845085 0.170 NA NA
TSC1 trametinib 0.1714196 0.4856889 0.17142 NA NA
PDGFB tretinoin 0.1722109 0.4533977 0.1722 NA NA
PTCH1 tretinoin 0.1733580 0.4533977 0.1734 NA NA
TSC2 crizotinib 0.1757036 0.6247240 0.176 NA NA
ESR1 nilotinib 0.1779167 0.7074000 0.178 NA NA
NPM1 cabozantinib 0.1781069 0.5046363 0.1781 NA NA
JAK2 docetaxel 0.1783666 0.6845085 0.178 NA NA
ERBB2 belinostat 0.1805478 0.6734824 0.181 NA NA
G6PD docetaxel 0.1822668 0.6845085 0.182 NA NA
MYD88 afatinib 0.1823685 0.5609625 0.182 NA NA
KRAS afatinib 0.1838668 0.5609625 0.184 NA NA
MET afatinib 0.1841947 0.5609625 0.184 NA NA
ABL1 belinostat 0.1850848 0.6734824 0.185 NA NA
DNMT3A nilotinib 0.1870610 0.7074000 0.187 NA NA
PTCH1 bosutinib monohydrate 0.1879013 0.8678254 0.188 NA NA
BRCA2 alectinib 0.1881297 0.6998836 0.188 NA NA
ALK sunitinib 0.1882759 0.8777619 0.188 NA NA
ALK afatinib 0.1885392 0.5609625 0.189 NA NA
TSC2 sunitinib 0.1886391 0.8777619 0.189 NA NA
PDGFRB dabrafenib 0.1895855 0.5719432 0.18959 NA NA
TSC1 midostaurin 0.1925940 0.6011299 0.1926 NA NA
NPM1 imatinib 0.1935911 0.7846892 0.194 NA NA
NPM1 midostaurin 0.1944832 0.6011299 0.1945 NA NA
MGMT cabozantinib 0.1946541 0.5090952 0.1947 NA NA
PML alectinib 0.1950849 0.6998836 0.195 NA NA
FLT3 rucaparib 0.1971116 0.8376438 0.197 NA NA
BRAF ruxolitinib 0.1976786 0.9414093 0.198 NA NA
TSC2 docetaxel 0.1988212 0.6845085 0.199 NA NA
JAK2 gefitinib 0.1997501 0.7564295 0.200 NA NA
ESR1 gefitinib 0.2002313 0.7564295 0.200 NA NA
UGT1A1 palbociclib 0.2007621 0.7307299 0.2008 NA NA
PML palbociclib 0.2018104 0.7307299 0.2018 NA NA
KIT dabrafenib 0.2027554 0.5719432 0.20276 NA NA
PDGFRB erlotinib 0.2028937 0.7675082 0.203 NA NA
IDH2 vismodegib 0.2041835 0.8104430 0.20 NA NA
TSC2 ponatinib 0.2051961 0.9972326 0.205 NA NA
FLT3 dasatinib 0.2059686 0.9447103 0.206 NA NA
JAK2 temozolomide 0.2108300 0.6854745 0.2108 NA NA
G6PD afatinib 0.2109900 0.5609625 0.211 NA NA
EGFR crizotinib 0.2113667 0.6763734 0.211 NA NA
ABL1 palbociclib 0.2122468 0.7307299 0.2122 NA NA
ESR1 palbociclib 0.2146985 0.7307299 0.2147 NA NA
NPM1 erlotinib 0.2158617 0.7675082 0.216 NA NA
PTCH1 temozolomide 0.2167760 0.6854745 0.2168 NA NA
ESR1 belinostat 0.2186179 0.6734824 0.219 NA NA
MYD88 belinostat 0.2195610 0.6734824 0.220 NA NA
BRCA1 vismodegib 0.2203270 0.8104430 0.22 NA NA
PML temozolomide 0.2217712 0.6854745 0.2218 NA NA
PDGFB vismodegib 0.2233811 0.8104430 0.22 NA NA
ROS1 belinostat 0.2235080 0.6734824 0.224 NA NA
KRAS tretinoin 0.2237016 0.5432753 0.2237 NA NA
NPM1 alectinib 0.2246258 0.6998836 0.225 NA NA
DNMT3A dasatinib 0.2260896 0.9447103 0.226 NA NA
ALK ruxolitinib 0.2294413 0.9414093 0.229 NA NA
PDGFB afatinib 0.2297013 0.5609625 0.230 NA NA
DPYD ponatinib 0.2315390 0.9972326 0.232 NA NA
G6PD ruxolitinib 0.2325984 0.9414093 0.233 NA NA
KIT vismodegib 0.2326739 0.8104430 0.23 NA NA
ROS1 alectinib 0.2335276 0.6998836 0.234 NA NA
ABL1 lapatinib 0.2342922 0.8759947 0.234 NA NA
PTCH1 belinostat 0.2376997 0.6734824 0.238 NA NA
JAK2 alectinib 0.2385373 0.6998836 0.239 NA NA
MGMT palbociclib 0.2389195 0.7307299 0.2389 NA NA
TSC2 alectinib 0.2393143 0.6998836 0.239 NA NA
CYP19A1 trametinib 0.2426959 0.5652949 0.24270 NA NA
G6PD dabrafenib 0.2443393 0.5719432 0.24434 NA NA
DPYD trametinib 0.2458254 0.5652949 0.24583 NA NA
G6PD vismodegib 0.2460143 0.8104430 0.25 NA NA
ABL1 alectinib 0.2470177 0.6998836 0.247 NA NA
TSC1 vismodegib 0.2476631 0.8104430 0.25 NA NA
IDH2 bosutinib monohydrate 0.2477175 0.8678254 0.248 NA NA
ERBB3 tretinoin 0.2487626 0.5613501 0.2488 NA NA
NPM1 docetaxel 0.2488101 0.6845085 0.249 NA NA
RET trametinib 0.2493948 0.5652949 0.24939 NA NA
KRAS ponatinib 0.2496849 0.9972326 0.250 NA NA
PDGFB imatinib 0.2502352 0.7846892 0.250 NA NA
MGMT lapatinib 0.2514271 0.8759947 0.251 NA NA
ERBB2 dabrafenib 0.2521562 0.5719432 0.25216 NA NA
PML dabrafenib 0.2532744 0.5719432 0.25327 NA NA
EGFR dasatinib 0.2539632 0.9447103 0.254 NA NA
BRCA1 bosutinib monohydrate 0.2550079 0.8678254 0.255 NA NA
BRCA2 afatinib 0.2553180 0.5609625 0.255 NA NA
PML sunitinib 0.2560406 0.8777619 0.256 NA NA
TSC2 palbociclib 0.2593090 0.7307299 0.2593 NA NA
IDH2 ponatinib 0.2610616 0.9972326 0.261 NA NA
PTCH1 ponatinib 0.2619913 0.9972326 0.262 NA NA
UGT1A1 crizotinib 0.2625570 0.7638022 0.263 NA NA
TPMT docetaxel 0.2626723 0.6845085 0.263 NA NA
PTCH1 afatinib 0.2628105 0.5609625 0.263 NA NA
PDGFB lapatinib 0.2633229 0.8759947 0.263 NA NA
DNMT3A afatinib 0.2639824 0.5609625 0.264 NA NA
TPMT vismodegib 0.2650075 0.8104430 0.27 NA NA
BRAF docetaxel 0.2666895 0.6845085 0.267 NA NA
MGMT midostaurin 0.2670503 0.7109289 0.2671 NA NA
DNMT3A cabozantinib 0.2687536 0.6476573 0.2688 NA NA
DPYD dabrafenib 0.2691497 0.5719432 0.26915 NA NA
JAK2 tretinoin 0.2702044 0.5613501 0.2702 NA NA
EGFR alectinib 0.2707820 0.7081991 0.271 NA NA
ALK belinostat 0.2712457 0.7094119 0.271 NA NA
AKT1 vismodegib 0.2724738 0.8104430 0.27 NA NA
KRAS docetaxel 0.2725554 0.6845085 0.273 NA NA
NPM1 lapatinib 0.2737483 0.8759947 0.274 NA NA
TPMT nilotinib 0.2737875 0.7074000 0.274 NA NA
PDGFRB nilotinib 0.2738696 0.7074000 0.274 NA NA
ERBB2 trametinib 0.2740267 0.5823067 0.27403 NA NA
NPM1 temozolomide 0.2747200 0.7783735 0.2747 NA NA
PDGFB nilotinib 0.2752114 0.7074000 0.275 NA NA
JAK2 rucaparib 0.2799799 0.8376438 0.280 NA NA
KIT ruxolitinib 0.2812051 0.9414093 0.281 NA NA
PDGFRB docetaxel 0.2818564 0.6845085 0.282 NA NA
PDGFRA midostaurin 0.2883532 0.7109289 0.2884 NA NA
ERBB3 cabozantinib 0.2887403 0.6476573 0.2887 NA NA
MET palbociclib 0.2900431 0.7307299 0.2900 NA NA
MET vismodegib 0.2900560 0.8104430 0.29 NA NA
BRCA2 bosutinib monohydrate 0.2907814 0.8678254 0.291 NA NA
KRAS midostaurin 0.2927354 0.7109289 0.2927 NA NA
PTCH1 pazopanib 0.2950783 0.9239682 0.2951 NA NA
AKT1 alectinib 0.2956410 0.7179852 0.296 NA NA
UGT1A1 imatinib 0.2957214 0.7846892 0.296 NA NA
ROS1 bosutinib monohydrate 0.2963370 0.8678254 0.296 NA NA
FLT3 trametinib 0.2977892 0.5955784 0.29779 NA NA
ERBB2 tretinoin 0.3012993 0.5613501 0.3013 NA NA
AKT1 ruxolitinib 0.3044779 0.9414093 0.304 NA NA
TSC1 ruxolitinib 0.3045736 0.9414093 0.305 NA NA
DPYD cabozantinib 0.3047799 0.6476573 0.3048 NA NA
IDH2 pazopanib 0.3050404 0.9239682 0.3050 NA NA
PTCH1 nilotinib 0.3093982 0.7074000 0.309 NA NA
BRAF tretinoin 0.3097622 0.5613501 0.3098 NA NA
DPYD afatinib 0.3107947 0.6215893 0.311 NA NA
TSC1 imatinib 0.3108496 0.7846892 0.311 NA NA
MGMT rucaparib 0.3112521 0.8376438 0.311 NA NA
TPMT gefitinib 0.3116629 0.8402334 0.312 NA NA
ERBB2 dasatinib 0.3121664 0.9447103 0.312 NA NA
MET tretinoin 0.3136957 0.5613501 0.3137 NA NA
BRCA2 rucaparib 0.3140675 0.8376438 0.314 NA NA
PDGFRA imatinib 0.3143676 0.7846892 0.314 NA NA
ESR1 rucaparib 0.3155348 0.8376438 0.316 NA NA
KRAS belinostat 0.3158051 0.7166062 0.316 NA NA
ERBB2 imatinib 0.3160344 0.7846892 0.316 NA NA
CYP19A1 belinostat 0.3161498 0.7166062 0.316 NA NA
KIT rucaparib 0.3176892 0.8376438 0.318 NA NA
PTCH1 palbociclib 0.3180803 0.7307299 0.3181 NA NA
NPM1 nilotinib 0.3197255 0.7074000 0.320 NA NA
ROS1 rucaparib 0.3210092 0.8376438 0.321 NA NA
DPYD vismodegib 0.3255816 0.8104430 0.33 NA NA
IDH2 gefitinib 0.3258316 0.8402334 0.326 NA NA
FLT3 gefitinib 0.3274937 0.8402334 0.327 NA NA
MGMT dabrafenib 0.3282216 0.6564431 0.32822 NA NA
IDH2 docetaxel 0.3288933 0.7454914 0.329 NA NA
TSC2 lapatinib 0.3311360 0.8842093 0.331 NA NA
EGFR midostaurin 0.3312793 0.7508998 0.3313 NA NA
ERBB2 nilotinib 0.3322155 0.7074000 0.332 NA NA
PDGFB palbociclib 0.3327587 0.7307299 0.3328 NA NA
ROS1 nilotinib 0.3339538 0.7074000 0.334 NA NA
ERBB3 gefitinib 0.3353927 0.8402334 0.335 NA NA
BRAF imatinib 0.3365852 0.7846892 0.337 NA NA
MET pazopanib 0.3375577 0.9239682 0.3376 NA NA
ERBB3 bosutinib monohydrate 0.3381914 0.8678254 0.338 NA NA
RET vismodegib 0.3393340 0.8104430 0.34 NA NA
ERBB2 pazopanib 0.3398016 0.9239682 0.3398 NA NA
PDGFRB rucaparib 0.3399009 0.8376438 0.340 NA NA
MYD88 rucaparib 0.3422485 0.8376438 0.342 NA NA
PDGFRB vismodegib 0.3432087 0.8104430 0.34 NA NA
DPYD rucaparib 0.3449122 0.8376438 0.345 NA NA
DPYD dasatinib 0.3450704 0.9447103 0.345 NA NA
BRCA1 alectinib 0.3475727 0.7403839 0.348 NA NA
MET bosutinib monohydrate 0.3480988 0.8678254 0.348 NA NA
IDH2 tretinoin 0.3482943 0.5921004 0.3483 NA NA
BRCA2 belinostat 0.3501637 0.7440979 0.350 NA NA
G6PD ponatinib 0.3562978 0.9972326 0.356 NA NA
CYP19A1 alectinib 0.3564980 0.7403839 0.356 NA NA
DNMT3A vismodegib 0.3575484 0.8104430 0.36 NA NA
PTCH1 docetaxel 0.3578141 0.7603550 0.358 NA NA
BRAF nilotinib 0.3579029 0.7074000 0.358 NA NA
KIT afatinib 0.3580267 0.6762727 0.358 NA NA
MET sunitinib 0.3589333 0.8777619 0.359 NA NA
BRAF pazopanib 0.3592410 0.9239682 0.3592 NA NA
PDGFB gefitinib 0.3627861 0.8402334 0.363 NA NA
BRCA2 pazopanib 0.3630866 0.9239682 0.3631 NA NA
PDGFRB lapatinib 0.3673411 0.8842093 0.367 NA NA
TSC1 lapatinib 0.3673411 0.8842093 0.367 NA NA
TSC1 pazopanib 0.3683895 0.9239682 0.3684 NA NA
ALK crizotinib 0.3700920 0.8464147 0.370 NA NA
TSC1 palbociclib 0.3704875 0.7307299 0.3705 NA NA
NPM1 crizotinib 0.3719543 0.8464147 0.372 NA NA
G6PD palbociclib 0.3728013 0.7307299 0.3728 NA NA
ERBB2 ruxolitinib 0.3785733 0.9526741 0.379 NA NA
PDGFB dasatinib 0.3792939 0.9447103 0.379 NA NA
KRAS dasatinib 0.3806744 0.9447103 0.381 NA NA
NPM1 bosutinib monohydrate 0.3813570 0.8678254 0.381 NA NA
G6PD nilotinib 0.3817505 0.7074000 0.382 NA NA
DPYD temozolomide 0.3822258 0.8360585 0.3822 NA NA
ERBB2 bosutinib monohydrate 0.3828642 0.8678254 0.383 NA NA
PTCH1 alectinib 0.3832319 0.7403839 0.383 NA NA
BRCA2 gefitinib 0.3852457 0.8402334 0.385 NA NA
RET imatinib 0.3884249 0.7846892 0.388 NA NA
ERBB3 belinostat 0.3891061 0.7587223 0.389 NA NA
BRCA1 dasatinib 0.3928660 0.9447103 0.393 NA NA
ESR1 vismodegib 0.3942862 0.8378583 0.39 NA NA
AKT1 tretinoin 0.3944562 0.6307930 0.3945 NA NA
ALK dasatinib 0.3952274 0.9447103 0.395 NA NA
TSC1 alectinib 0.3956481 0.7403839 0.396 NA NA
AKT1 palbociclib 0.3961746 0.7307299 0.3962 NA NA
CYP19A1 nilotinib 0.3966596 0.7074000 0.397 NA NA
MET gefitinib 0.3977576 0.8402334 0.398 NA NA
UGT1A1 pazopanib 0.3984406 0.9239682 0.3984 NA NA
UGT1A1 sunitinib 0.3992388 0.8777619 0.399 NA NA
NPM1 palbociclib 0.3996308 0.7307299 0.3996 NA NA
ALK temozolomide 0.4005886 0.8360585 0.4006 NA NA
KIT erlotinib 0.4010455 0.8537089 0.401 NA NA
IDH2 crizotinib 0.4011464 0.8464147 0.401 NA NA
PDGFRA rucaparib 0.4016231 0.8467310 0.402 NA NA
UGT1A1 belinostat 0.4041103 0.7587223 0.404 NA NA
BRCA1 erlotinib 0.4046403 0.8537089 0.405 NA NA
PDGFB docetaxel 0.4060325 0.8036630 0.406 NA NA
PML tretinoin 0.4081601 0.6307930 0.4082 NA NA
DNMT3A ruxolitinib 0.4082055 0.9526741 0.408 NA NA
PDGFRA palbociclib 0.4083491 0.7307299 0.4083 NA NA
IDH2 trametinib 0.4108540 0.7083580 0.41085 NA NA
RET lapatinib 0.4130391 0.8842093 0.413 NA NA
BRCA1 trametinib 0.4159320 0.7083580 0.41593 NA NA
DNMT3A trametinib 0.4166812 0.7083580 0.41668 NA NA
PDGFRA bosutinib monohydrate 0.4175132 0.8678810 0.418 NA NA
MYD88 alectinib 0.4215902 0.7403839 0.422 NA NA
BRCA1 nilotinib 0.4230452 0.7074000 0.423 NA NA
JAK2 belinostat 0.4239919 0.7587223 0.424 NA NA
ABL1 dasatinib 0.4250747 0.9447103 0.425 NA NA
ESR1 docetaxel 0.4254686 0.8036630 0.425 NA NA
ERBB2 erlotinib 0.4285389 0.8537089 0.429 NA NA
KIT gefitinib 0.4300536 0.8402334 0.430 NA NA
BRCA1 ponatinib 0.4319743 0.9972326 0.432 NA NA
UGT1A1 dasatinib 0.4335809 0.9447103 0.434 NA NA
PDGFB temozolomide 0.4372281 0.8360585 0.4372 NA NA
DNMT3A imatinib 0.4384088 0.7846892 0.438 NA NA
FLT3 temozolomide 0.4398840 0.8360585 0.4399 NA NA
IDH2 lapatinib 0.4399819 0.8842093 0.440 NA NA
CYP19A1 midostaurin 0.4400719 0.7825538 0.4401 NA NA
ABL1 rucaparib 0.4411690 0.8467310 0.441 NA NA
ABL1 dabrafenib 0.4415642 0.7445082 0.44156 NA NA
KIT lapatinib 0.4421047 0.8842093 0.442 NA NA
FLT3 pazopanib 0.4421181 0.9239682 0.4421 NA NA
TSC2 pazopanib 0.4425985 0.9239682 0.4426 NA NA
MYD88 nilotinib 0.4453090 0.7074000 0.445 NA NA
ERBB2 alectinib 0.4455420 0.7403839 0.446 NA NA
ESR1 crizotinib 0.4487601 0.8464147 0.449 NA NA
AKT1 dabrafenib 0.4490980 0.7445082 0.44910 NA NA
PDGFRA dabrafenib 0.4498286 0.7445082 0.44983 NA NA
RET afatinib 0.4515995 0.7810241 0.452 NA NA
G6PD tretinoin 0.4531080 0.6698118 0.4531 NA NA
CYP19A1 dasatinib 0.4547296 0.9447103 0.455 NA NA
PML bosutinib monohydrate 0.4556704 0.8678810 0.456 NA NA
KRAS vismodegib 0.4564329 0.8556940 0.46 NA NA
PDGFB alectinib 0.4572960 0.7403839 0.457 NA NA
CYP19A1 erlotinib 0.4583847 0.8537089 0.458 NA NA
MYD88 temozolomide 0.4590388 0.8360585 0.4590 NA NA
EGFR afatinib 0.4594260 0.7810241 0.459 NA NA
DPYD bosutinib monohydrate 0.4594664 0.8678810 0.459 NA NA
KIT temozolomide 0.4596217 0.8360585 0.4596 NA NA
PML imatinib 0.4608455 0.7846892 0.461 NA NA
JAK2 nilotinib 0.4609294 0.7074000 0.461 NA NA
DNMT3A erlotinib 0.4620527 0.8537089 0.462 NA NA
MET nilotinib 0.4620552 0.7074000 0.462 NA NA
BRCA1 belinostat 0.4653768 0.7769514 0.465 NA NA
BRAF erlotinib 0.4662981 0.8537089 0.466 NA NA
PTCH1 sunitinib 0.4664159 0.8777619 0.466 NA NA
ABL1 imatinib 0.4679914 0.7846892 0.468 NA NA
BRCA2 ponatinib 0.4687534 0.9972326 0.469 NA NA
PDGFRB imatinib 0.4699331 0.7846892 0.470 NA NA
PTCH1 dasatinib 0.4723552 0.9447103 0.472 NA NA
ERBB3 palbociclib 0.4724268 0.8031255 0.4724 NA NA
MGMT trametinib 0.4735520 0.7350776 0.47355 NA NA
TSC2 vismodegib 0.4749053 0.8556940 0.47 NA NA
PTCH1 trametinib 0.4756384 0.7350776 0.47564 NA NA
PDGFRB ruxolitinib 0.4765472 0.9526741 0.477 NA NA
MYD88 tretinoin 0.4774785 0.6757916 0.4775 NA NA
PDGFRA vismodegib 0.4781819 0.8556940 0.48 NA NA
BRCA1 ruxolitinib 0.4788739 0.9526741 0.479 NA NA
G6PD belinostat 0.4798818 0.7769514 0.480 NA NA
NPM1 ruxolitinib 0.4817234 0.9526741 0.482 NA NA
PDGFB ponatinib 0.4821695 0.9972326 0.482 NA NA
G6PD pazopanib 0.4835225 0.9239682 0.4835 NA NA
ERBB2 sunitinib 0.4842706 0.8777619 0.484 NA NA
CYP19A1 docetaxel 0.4847190 0.8413401 0.485 NA NA
PDGFRB afatinib 0.4872986 0.7852762 0.487 NA NA
KRAS erlotinib 0.4886809 0.8537089 0.489 NA NA
TSC2 erlotinib 0.4915604 0.8537089 0.492 NA NA
UGT1A1 nilotinib 0.4918297 0.7074000 0.492 NA NA
CYP19A1 lapatinib 0.4937490 0.8940161 0.494 NA NA
PDGFB dabrafenib 0.4964153 0.7445082 0.49642 NA NA
RET tretinoin 0.4969056 0.6757916 0.4969 NA NA
ALK midostaurin 0.4971993 0.7825538 0.4972 NA NA
MYD88 cabozantinib 0.4985291 0.9262331 0.4985 NA NA
PDGFRA sunitinib 0.4989956 0.8777619 0.499 NA NA
RET cabozantinib 0.4991041 0.9262331 0.4991 NA NA
EGFR nilotinib 0.4993412 0.7074000 0.499 NA NA
ERBB3 lapatinib 0.5028840 0.8940161 0.503 NA NA
RET crizotinib 0.5041789 0.8464147 0.504 NA NA
G6PD imatinib 0.5046668 0.7846892 0.505 NA NA
CYP19A1 afatinib 0.5081199 0.7852762 0.508 NA NA
DPYD midostaurin 0.5086465 0.7825538 0.5086 NA NA
ERBB3 pazopanib 0.5092517 0.9239682 0.5093 NA NA
PDGFRB sunitinib 0.5106193 0.8777619 0.511 NA NA
BRAF gefitinib 0.5109860 0.8402334 0.511 NA NA
PML vismodegib 0.5118312 0.8701130 0.51 NA NA
KIT palbociclib 0.5134101 0.8071578 0.5134 NA NA
ESR1 dabrafenib 0.5138002 0.7445082 0.51380 NA NA
PDGFRB temozolomide 0.5143244 0.8360585 0.5143 NA NA
FLT3 dabrafenib 0.5153597 0.7445082 0.51536 NA NA
MYD88 dasatinib 0.5156798 0.9470657 0.516 NA NA
DPYD ruxolitinib 0.5165164 0.9526741 0.517 NA NA
CYP19A1 sunitinib 0.5166344 0.8777619 0.517 NA NA
DPYD docetaxel 0.5183010 0.8413401 0.518 NA NA
MET docetaxel 0.5196512 0.8413401 0.520 NA NA
DPYD imatinib 0.5204070 0.7846892 0.520 NA NA
EGFR imatinib 0.5206143 0.7846892 0.521 NA NA
DPYD gefitinib 0.5210093 0.8402334 0.521 NA NA
DNMT3A palbociclib 0.5222786 0.8071578 0.5223 NA NA
DPYD alectinib 0.5225472 0.8075729 0.523 NA NA
NPM1 gefitinib 0.5227495 0.8402334 0.523 NA NA
PDGFB trametinib 0.5280206 0.7805521 0.52802 NA NA
BRAF sunitinib 0.5308225 0.8777619 0.531 NA NA
MYD88 midostaurin 0.5310291 0.7825538 0.5310 NA NA
ABL1 crizotinib 0.5320626 0.8464147 0.532 NA NA
DNMT3A midostaurin 0.5328652 0.7825538 0.5329 NA NA
G6PD erlotinib 0.5348254 0.8537089 0.535 NA NA
ERBB3 nilotinib 0.5371710 0.7305525 0.537 NA NA
TSC2 imatinib 0.5394738 0.7846892 0.539 NA NA
ABL1 midostaurin 0.5402282 0.7825538 0.5402 NA NA
PTCH1 dabrafenib 0.5410696 0.7445082 0.54107 NA NA
BRCA2 temozolomide 0.5416844 0.8360585 0.5417 NA NA
TSC2 midostaurin 0.5433441 0.7825538 0.5433 NA NA
AKT1 cabozantinib 0.5467795 0.9262331 0.5468 NA NA
UGT1A1 dabrafenib 0.5474325 0.7445082 0.54743 NA NA
PDGFB midostaurin 0.5477164 0.7825538 0.5477 NA NA
UGT1A1 temozolomide 0.5500430 0.8360585 0.5500 NA NA
MGMT crizotinib 0.5501355 0.8464147 0.550 NA NA
RET bosutinib monohydrate 0.5509591 0.9571596 0.551 NA NA
ROS1 gefitinib 0.5511895 0.8402334 0.551 NA NA
KIT midostaurin 0.5523909 0.7825538 0.5524 NA NA
CYP19A1 pazopanib 0.5523979 0.9239682 0.5524 NA NA
ERBB3 alectinib 0.5530430 0.8175419 0.553 NA NA
MGMT ponatinib 0.5540759 0.9972326 0.554 NA NA
AKT1 belinostat 0.5547454 0.8458949 0.555 NA NA
CYP19A1 tretinoin 0.5549899 0.7257560 0.5550 NA NA
TSC2 dasatinib 0.5568736 0.9470657 0.557 NA NA
MYD88 erlotinib 0.5580136 0.8537089 0.558 NA NA
ALK pazopanib 0.5581192 0.9239682 0.5581 NA NA
RET ruxolitinib 0.5597886 0.9526741 0.560 NA NA
ERBB3 afatinib 0.5614904 0.7959129 0.561 NA NA
JAK2 crizotinib 0.5623793 0.8464147 0.562 NA NA
PML pazopanib 0.5629739 0.9239682 0.5630 NA NA
TSC1 erlotinib 0.5633547 0.8537089 0.563 NA NA
ESR1 sunitinib 0.5643768 0.8777619 0.564 NA NA
G6PD rucaparib 0.5646292 0.8467310 0.565 NA NA
RET gefitinib 0.5658382 0.8402334 0.566 NA NA
PDGFB sunitinib 0.5660424 0.8777619 0.566 NA NA
FLT3 sunitinib 0.5660424 0.8777619 0.566 NA NA
EGFR temozolomide 0.5669345 0.8360585 0.5669 NA NA
JAK2 palbociclib 0.5675682 0.8390139 0.5676 NA NA
FLT3 lapatinib 0.5679419 0.9537394 0.568 NA NA
CYP19A1 gefitinib 0.5683932 0.8402334 0.568 NA NA
JAK2 erlotinib 0.5702807 0.8537089 0.570 NA NA
TSC2 cabozantinib 0.5710996 0.9262331 0.5711 NA NA
TSC1 docetaxel 0.5725527 0.8563752 0.573 NA NA
BRCA1 rucaparib 0.5768966 0.8467310 0.577 NA NA
MGMT belinostat 0.5789471 0.8458949 0.579 NA NA
ALK cabozantinib 0.5795396 0.9262331 0.5795 NA NA
ABL1 sunitinib 0.5802023 0.8777619 0.580 NA NA
ROS1 midostaurin 0.5814725 0.7908026 0.5815 NA NA
ALK rucaparib 0.5819295 0.8467310 0.582 NA NA
TSC2 ruxolitinib 0.5824296 0.9526741 0.582 NA NA
PDGFRA docetaxel 0.5828922 0.8563752 0.583 NA NA
PTCH1 ruxolitinib 0.5835921 0.9526741 0.584 NA NA
RET ponatinib 0.5838741 0.9972326 0.584 NA NA
RET nilotinib 0.5840052 0.7528214 0.584 NA NA
TSC2 rucaparib 0.5851976 0.8467310 0.585 NA NA
NPM1 ponatinib 0.5854791 0.9972326 0.585 NA NA
BRCA1 afatinib 0.5859699 0.7959129 0.586 NA NA
EGFR ponatinib 0.5861711 0.9972326 0.586 NA NA
PDGFRA temozolomide 0.5883211 0.8360585 0.5883 NA NA
EGFR tretinoin 0.5940328 0.7480413 0.5940 NA NA
KIT pazopanib 0.5948891 0.9239682 0.5949 NA NA
PML rucaparib 0.5950165 0.8467310 0.595 NA NA
EGFR lapatinib 0.5960871 0.9537394 0.596 NA NA
ESR1 ruxolitinib 0.5973766 0.9526741 0.597 NA NA
MGMT nilotinib 0.5978288 0.7528214 0.598 NA NA
MYD88 vismodegib 0.5979041 0.9680352 0.60 NA NA
ABL1 erlotinib 0.5985108 0.8537089 0.599 NA NA
IDH2 sunitinib 0.6010327 0.8777619 0.601 NA NA
CYP19A1 cabozantinib 0.6024929 0.9262331 0.6025 NA NA
BRAF crizotinib 0.6026642 0.8464147 0.603 NA NA
TPMT trametinib 0.6050220 0.8571145 0.60502 NA NA
ROS1 sunitinib 0.6060667 0.8777619 0.606 NA NA
BRCA2 docetaxel 0.6116514 0.8563752 0.612 NA NA
TSC1 ponatinib 0.6125905 0.9972326 0.613 NA NA
MET ponatinib 0.6159349 0.9972326 0.616 NA NA
PML ponatinib 0.6178115 0.9972326 0.618 NA NA
AKT1 pazopanib 0.6189835 0.9239682 0.6190 NA NA
CYP19A1 crizotinib 0.6190452 0.8464147 0.619 NA NA
JAK2 afatinib 0.6195384 0.7959129 0.620 NA NA
G6PD sunitinib 0.6213525 0.8777619 0.621 NA NA
MGMT bosutinib monohydrate 0.6222029 0.9571596 0.622 NA NA
KRAS gefitinib 0.6235545 0.8503583 0.624 NA NA
DNMT3A tretinoin 0.6240726 0.7578025 0.6241 NA NA
PDGFB belinostat 0.6267736 0.8458949 0.627 NA NA
MYD88 pazopanib 0.6271722 0.9239682 0.6272 NA NA
ROS1 afatinib 0.6282804 0.7959129 0.628 NA NA
TPMT afatinib 0.6320485 0.7959129 0.632 NA NA
AKT1 trametinib 0.6346674 0.8631476 0.63467 NA NA
EGFR sunitinib 0.6347395 0.8777619 0.635 NA NA
PML erlotinib 0.6360548 0.8537089 0.636 NA NA
PDGFRB alectinib 0.6381495 0.8897754 0.638 NA NA
KRAS imatinib 0.6424818 0.8938878 0.642 NA NA
MGMT sunitinib 0.6441449 0.8777619 0.644 NA NA
EGFR ruxolitinib 0.6447588 0.9526741 0.645 NA NA
NPM1 rucaparib 0.6448258 0.8467310 0.645 NA NA
DNMT3A ponatinib 0.6448404 0.9972326 0.645 NA NA
KRAS lapatinib 0.6456344 0.9838238 0.646 NA NA
UGT1A1 gefitinib 0.6466857 0.8503583 0.647 NA NA
PDGFB rucaparib 0.6469333 0.8467310 0.647 NA NA
ALK docetaxel 0.6482988 0.8563752 0.648 NA NA
FLT3 midostaurin 0.6526579 0.8534757 0.6527 NA NA
ERBB3 ruxolitinib 0.6532068 0.9526741 0.653 NA NA
TSC2 gefitinib 0.6534622 0.8503583 0.653 NA NA
UGT1A1 alectinib 0.6542466 0.8897754 0.654 NA NA
TSC2 bosutinib monohydrate 0.6543872 0.9571596 0.654 NA NA
KIT sunitinib 0.6583214 0.8777619 0.658 NA NA
PTCH1 crizotinib 0.6605698 0.8464147 0.661 NA NA
PDGFRB pazopanib 0.6626651 0.9239682 0.6627 NA NA
IDH2 erlotinib 0.6634451 0.8537089 0.663 NA NA
MGMT temozolomide 0.6634570 0.8360585 0.6635 NA NA
ALK vismodegib 0.6657344 0.9773105 0.67 NA NA
MYD88 palbociclib 0.6675616 0.8695381 0.6676 NA NA
FLT3 belinostat 0.6686147 0.8458949 0.669 NA NA
FLT3 ponatinib 0.6691061 0.9972326 0.669 NA NA
PDGFB crizotinib 0.6714054 0.8464147 0.671 NA NA
DNMT3A dabrafenib 0.6722330 0.8621835 0.67223 NA NA
FLT3 docetaxel 0.6723654 0.8563752 0.672 NA NA
MET ruxolitinib 0.6724758 0.9526741 0.672 NA NA
ROS1 pazopanib 0.6730661 0.9239682 0.6731 NA NA
BRCA2 nilotinib 0.6739857 0.8184112 0.674 NA NA
ROS1 temozolomide 0.6751390 0.8360585 0.6751 NA NA
PML gefitinib 0.6752846 0.8503583 0.675 NA NA
AKT1 rucaparib 0.6766724 0.8467310 0.677 NA NA
EGFR belinostat 0.6783585 0.8458949 0.678 NA NA
ESR1 dasatinib 0.6806688 0.9470657 0.681 NA NA
ERBB2 rucaparib 0.6813788 0.8467310 0.681 NA NA
ESR1 lapatinib 0.6823352 0.9878187 0.682 NA NA
PDGFRA erlotinib 0.6824019 0.8537089 0.682 NA NA
PDGFRA ponatinib 0.6831311 0.9972326 0.683 NA NA
ESR1 midostaurin 0.6835330 0.8607452 0.6835 NA NA
ESR1 tretinoin 0.6845216 0.8025426 0.6845 NA NA
BRCA1 dabrafenib 0.6846752 0.8621835 0.68468 NA NA
KRAS rucaparib 0.6847697 0.8467310 0.685 NA NA
BRCA1 palbociclib 0.6850052 0.8695381 0.6850 NA NA
RET belinostat 0.6890334 0.8458949 0.689 NA NA
MYD88 crizotinib 0.6896981 0.8464147 0.690 NA NA
CYP19A1 bosutinib monohydrate 0.6902719 0.9571596 0.690 NA NA
MGMT pazopanib 0.6911734 0.9239682 0.6912 NA NA
ALK alectinib 0.6916060 0.9044078 0.692 NA NA
MET temozolomide 0.6917583 0.8360585 0.6918 NA NA
ESR1 pazopanib 0.6922709 0.9239682 0.6923 NA NA
PML cabozantinib 0.6926459 0.9262331 0.6926 NA NA
PDGFB cabozantinib 0.6931930 0.9262331 0.6932 NA NA
ESR1 trametinib 0.6962680 0.9060078 0.69627 NA NA
ABL1 temozolomide 0.6987934 0.8360585 0.6988 NA NA
BRCA1 docetaxel 0.7001716 0.8563752 0.700 NA NA
BRCA1 crizotinib 0.7017863 0.8464147 0.702 NA NA
PML dasatinib 0.7028188 0.9470657 0.703 NA NA
TSC1 dasatinib 0.7033301 0.9470657 0.703 NA NA
G6PD crizotinib 0.7040235 0.8464147 0.704 NA NA
ERBB2 docetaxel 0.7052502 0.8563752 0.705 NA NA
UGT1A1 ponatinib 0.7062488 0.9972326 0.706 NA NA
ROS1 vismodegib 0.7075936 0.9773105 0.71 NA NA
ERBB2 palbociclib 0.7083268 0.8695381 0.7083 NA NA
TSC2 belinostat 0.7089749 0.8458949 0.709 NA NA
IDH2 dasatinib 0.7090830 0.9470657 0.709 NA NA
ALK erlotinib 0.7099827 0.8537089 0.710 NA NA
JAK2 dasatinib 0.7103203 0.9470657 0.710 NA NA
RET dabrafenib 0.7105653 0.8628293 0.71057 NA NA
KRAS ruxolitinib 0.7137147 0.9613264 0.714 NA NA
RET dasatinib 0.7148163 0.9470657 0.715 NA NA
KRAS temozolomide 0.7150718 0.8360585 0.7151 NA NA
ESR1 cabozantinib 0.7161231 0.9262331 0.7161 NA NA
EGFR trametinib 0.7194767 0.9060078 0.71948 NA NA
PTCH1 vismodegib 0.7198241 0.9773105 0.72 NA NA
ERBB3 erlotinib 0.7203169 0.8537089 0.720 NA NA
PDGFRB ponatinib 0.7240517 0.9972326 0.724 NA NA
ERBB3 rucaparib 0.7263040 0.8467310 0.726 NA NA
DPYD pazopanib 0.7284979 0.9239682 0.7285 NA NA
PDGFRB palbociclib 0.7303977 0.8695381 0.7304 NA NA
ABL1 nilotinib 0.7313136 0.8564973 0.731 NA NA
BRAF temozolomide 0.7335771 0.8360585 0.7336 NA NA
EGFR docetaxel 0.7346881 0.8613585 0.735 NA NA
PML ruxolitinib 0.7351319 0.9613264 0.735 NA NA
CYP19A1 temozolomide 0.7376987 0.8360585 0.7377 NA NA
PDGFRA crizotinib 0.7390621 0.8464147 0.739 NA NA
KIT dasatinib 0.7398951 0.9470657 0.740 NA NA
ERBB3 crizotinib 0.7406129 0.8464147 0.741 NA NA
AKT1 afatinib 0.7407741 0.8995114 0.741 NA NA
DPYD sunitinib 0.7414533 0.8823886 0.741 NA NA
KRAS bosutinib monohydrate 0.7414809 0.9571596 0.741 NA NA
TSC1 cabozantinib 0.7417760 0.9262331 0.7418 NA NA
CYP19A1 ponatinib 0.7421820 0.9972326 0.742 NA NA
PDGFRB bosutinib monohydrate 0.7429860 0.9571596 0.743 NA NA
IDH2 imatinib 0.7435570 0.9237192 0.744 NA NA
PDGFRA belinostat 0.7450686 0.8458949 0.745 NA NA
TPMT midostaurin 0.7460273 0.9058903 0.7460 NA NA
PML belinostat 0.7463779 0.8458949 0.746 NA NA
BRCA1 imatinib 0.7500850 0.9237192 0.750 NA NA
KIT imatinib 0.7505218 0.9237192 0.751 NA NA
EGFR palbociclib 0.7514260 0.8695381 0.7514 NA NA
DNMT3A bosutinib monohydrate 0.7551741 0.9571596 0.755 NA NA
PDGFRA pazopanib 0.7583124 0.9239682 0.7583 NA NA
DNMT3A alectinib 0.7613631 0.9273034 0.761 NA NA
TPMT rucaparib 0.7619662 0.8467310 0.762 NA NA
AKT1 gefitinib 0.7625117 0.8968173 0.763 NA NA
G6PD trametinib 0.7631155 0.9266403 0.76312 NA NA
EGFR pazopanib 0.7631271 0.9239682 0.7631 NA NA
MGMT alectinib 0.7636616 0.9273034 0.764 NA NA
PDGFB bosutinib monohydrate 0.7637526 0.9571596 0.764 NA NA
KRAS sunitinib 0.7642647 0.8823886 0.764 NA NA
PDGFRB gefitinib 0.7649324 0.8968173 0.765 NA NA
TPMT palbociclib 0.7668585 0.8695381 0.7669 NA NA
CYP19A1 palbociclib 0.7672395 0.8695381 0.7672 NA NA
ABL1 bosutinib monohydrate 0.7708229 0.9571596 0.771 NA NA
ALK tretinoin 0.7717427 0.8561605 0.7717 NA NA
PDGFRA lapatinib 0.7718304 0.9878187 0.772 NA NA
PDGFB pazopanib 0.7722360 0.9239682 0.7722 NA NA
ERBB2 vismodegib 0.7739594 0.9773105 0.77 NA NA
KIT docetaxel 0.7747007 0.8752363 0.775 NA NA
PML nilotinib 0.7777017 0.8564973 0.778 NA NA
DPYD tretinoin 0.7806169 0.8561605 0.7806 NA NA
EGFR rucaparib 0.7812134 0.8467310 0.781 NA NA
MET rucaparib 0.7852325 0.8467310 0.785 NA NA
MYD88 sunitinib 0.7863908 0.8823886 0.786 NA NA
NPM1 pazopanib 0.7880905 0.9239682 0.7881 NA NA
MYD88 imatinib 0.7889569 0.9350600 0.789 NA NA
BRAF ponatinib 0.7903167 0.9972326 0.790 NA NA
BRCA1 gefitinib 0.7929047 0.8986254 0.793 NA NA
FLT3 alectinib 0.7941809 0.9311087 0.794 NA NA
BRAF midostaurin 0.7943467 0.9164533 0.7943 NA NA
PTCH1 rucaparib 0.7952280 0.8467310 0.795 NA NA
PML docetaxel 0.7980096 0.8752363 0.798 NA NA
NPM1 vismodegib 0.8004491 0.9773105 0.80 NA NA
PDGFRB crizotinib 0.8019048 0.8848604 0.802 NA NA
ERBB3 trametinib 0.8021031 0.9403967 0.80210 NA NA
FLT3 afatinib 0.8043606 0.9430434 0.804 NA NA
JAK2 sunitinib 0.8046870 0.8823886 0.805 NA NA
KRAS nilotinib 0.8058237 0.8564973 0.806 NA NA
IDH2 nilotinib 0.8061151 0.8564973 0.806 NA NA
IDH2 ruxolitinib 0.8073056 0.9802461 0.807 NA NA
FLT3 tretinoin 0.8076478 0.8581257 0.8076 NA NA
DPYD belinostat 0.8079628 0.8861528 0.808 NA NA
ESR1 bosutinib monohydrate 0.8080005 0.9571596 0.808 NA NA
DPYD palbociclib 0.8111267 0.8753990 0.8111 NA NA
RET temozolomide 0.8158960 0.8948536 0.8159 NA NA
BRCA1 sunitinib 0.8165800 0.8823886 0.817 NA NA
ERBB3 dasatinib 0.8180600 0.9608776 0.818 NA NA
G6PD gefitinib 0.8202116 0.8995869 0.820 NA NA
FLT3 palbociclib 0.8239049 0.8753990 0.8239 NA NA
EGFR bosutinib monohydrate 0.8249647 0.9571596 0.825 NA NA
BRCA1 pazopanib 0.8251732 0.9335674 0.8252 NA NA
MET alectinib 0.8262519 0.9364188 0.826 NA NA
G6PD cabozantinib 0.8264953 0.9262331 0.8265 NA NA
ERBB3 sunitinib 0.8272393 0.8823886 0.827 NA NA
TSC1 dabrafenib 0.8273766 0.9700277 0.82738 NA NA
G6PD midostaurin 0.8281741 0.9164533 0.8282 NA NA
FLT3 ruxolitinib 0.8312495 0.9802461 0.831 NA NA
MET trametinib 0.8326323 0.9436500 0.83263 NA NA
BRCA1 cabozantinib 0.8333837 0.9262331 0.8334 NA NA
MGMT tretinoin 0.8349168 0.8602173 0.8349 NA NA
CYP19A1 rucaparib 0.8350834 0.8467310 0.835 NA NA
MET midostaurin 0.8355898 0.9164533 0.8356 NA NA
RET rucaparib 0.8423514 0.8467310 0.842 NA NA
BRAF cabozantinib 0.8425783 0.9262331 0.8426 NA NA
UGT1A1 rucaparib 0.8467310 0.8467310 0.847 NA NA
ERBB2 cabozantinib 0.8477762 0.9262331 0.8478 NA NA
DNMT3A gefitinib 0.8493836 0.9024701 0.849 NA NA
TPMT pazopanib 0.8511938 0.9335674 0.8512 NA NA
KRAS crizotinib 0.8512775 0.8966708 0.851 NA NA
MET cabozantinib 0.8512783 0.9262331 0.8513 NA NA
JAK2 imatinib 0.8521878 0.9739289 0.852 NA NA
JAK2 ruxolitinib 0.8523436 0.9802461 0.852 NA NA
UGT1A1 docetaxel 0.8542969 0.9076904 0.854 NA NA
MET dasatinib 0.8543593 0.9608776 0.854 NA NA
MYD88 ponatinib 0.8552763 0.9972326 0.855 NA NA
DPYD erlotinib 0.8604995 0.9307227 0.860 NA NA
ROS1 palbociclib 0.8627523 0.8888963 0.8628 NA NA
NPM1 afatinib 0.8655570 0.9518372 0.866 NA NA
UGT1A1 vismodegib 0.8657073 0.9773105 0.87 NA NA
NPM1 trametinib 0.8672776 0.9512077 0.86728 NA NA
G6PD dasatinib 0.8682306 0.9608776 0.868 NA NA
KIT crizotinib 0.8686498 0.8966708 0.869 NA NA
ESR1 ponatinib 0.8695576 0.9972326 0.870 NA NA
FLT3 bosutinib monohydrate 0.8695733 0.9571596 0.870 NA NA
DNMT3A lapatinib 0.8744953 0.9878187 0.874 NA NA
ERBB3 vismodegib 0.8804120 0.9773105 0.88 NA NA
TPMT ponatinib 0.8811769 0.9972326 0.881 NA NA
JAK2 midostaurin 0.8847197 0.9400146 0.8847 NA NA
BRCA2 ruxolitinib 0.8869414 0.9802461 0.887 NA NA
MYD88 bosutinib monohydrate 0.8892270 0.9571596 0.889 NA NA
JAK2 pazopanib 0.8907594 0.9464319 0.8908 NA NA
BRCA2 cabozantinib 0.8957636 0.9262331 0.8958 NA NA
ROS1 ruxolitinib 0.8966205 0.9802461 0.897 NA NA
PML trametinib 0.8969530 0.9530125 0.89695 NA NA
MGMT dasatinib 0.8989855 0.9608776 0.899 NA NA
ALK bosutinib monohydrate 0.9008561 0.9571596 0.901 NA NA
BRAF afatinib 0.9011510 0.9518372 0.901 NA NA
ROS1 crizotinib 0.9055984 0.9055984 0.906 NA NA
TSC1 belinostat 0.9061702 0.9585223 0.906 NA NA
ERBB3 docetaxel 0.9076995 0.9352056 0.908 NA NA
AKT1 temozolomide 0.9094399 0.9462406 0.9094 NA NA
CYP19A1 dabrafenib 0.9107211 0.9949724 0.91072 NA NA
ESR1 erlotinib 0.9133687 0.9307227 0.913 NA NA
BRCA1 lapatinib 0.9142372 0.9878187 0.914 NA NA
EGFR cabozantinib 0.9149328 0.9262331 0.9149 NA NA
BRCA2 sunitinib 0.9151190 0.9216497 0.915 NA NA
TSC1 gefitinib 0.9156569 0.9283678 0.916 NA NA
ROS1 imatinib 0.9157097 1.0000000 0.916 NA NA
ERBB2 ponatinib 0.9158815 0.9972326 0.916 NA NA
BRAF dasatinib 0.9161548 0.9608776 0.916 NA NA
BRCA2 dabrafenib 0.9168207 0.9949724 0.91682 NA NA
G6PD temozolomide 0.9184100 0.9462406 0.9184 NA NA
JAK2 lapatinib 0.9210478 0.9878187 0.921 NA NA
NPM1 sunitinib 0.9216497 0.9216497 0.922 NA NA
EGFR erlotinib 0.9218958 0.9307227 0.922 NA NA
ROS1 lapatinib 0.9246091 0.9878187 0.925 NA NA
PDGFRA ruxolitinib 0.9248089 0.9802461 0.925 NA NA
KIT ponatinib 0.9257639 0.9972326 0.926 NA NA
UGT1A1 cabozantinib 0.9262331 0.9262331 0.9262 NA NA
PTCH1 erlotinib 0.9275623 0.9307227 0.928 NA NA
MYD88 gefitinib 0.9283678 0.9283678 0.928 NA NA
TSC1 afatinib 0.9300086 0.9518372 0.930 NA NA
KIT belinostat 0.9303304 0.9585223 0.930 NA NA
RET erlotinib 0.9307227 0.9307227 0.931 NA NA
MYD88 lapatinib 0.9323054 0.9878187 0.932 NA NA
MGMT vismodegib 0.9400649 0.9773105 0.94 NA NA
BRCA2 trametinib 0.9421624 0.9707128 0.94216 NA NA
IDH2 alectinib 0.9437117 0.9851623 0.944 NA NA
IDH2 palbociclib 0.9443484 0.9443484 0.9443 NA NA
EGFR vismodegib 0.9448114 0.9773105 0.94 NA NA
IDH2 midostaurin 0.9477169 0.9563247 0.9477 NA NA
ABL1 vismodegib 0.9494533 0.9773105 0.95 NA NA
ESR1 afatinib 0.9503087 0.9518372 0.950 NA NA
ROS1 dasatinib 0.9512458 0.9608776 0.951 NA NA
UGT1A1 ruxolitinib 0.9514154 0.9802461 0.951 NA NA
PML afatinib 0.9518372 0.9518372 0.952 NA NA
BRCA1 temozolomide 0.9526259 0.9526259 0.9526 NA NA
ALK ponatinib 0.9561548 0.9972326 0.956 NA NA
RET midostaurin 0.9563247 0.9563247 0.9563 NA NA
BRAF lapatinib 0.9579143 0.9878187 0.958 NA NA
RET pazopanib 0.9581510 0.9871859 0.9582 NA NA
PDGFRA dasatinib 0.9608776 0.9608776 0.961 NA NA
ALK imatinib 0.9639389 1.0000000 0.964 NA NA
DPYD nilotinib 0.9678380 0.9911966 0.968 NA NA
G6PD lapatinib 0.9681165 0.9878187 0.968 NA NA
IDH2 dabrafenib 0.9684328 0.9949724 0.96843 NA NA
KIT alectinib 0.9692528 0.9851623 0.969 NA NA
AKT1 docetaxel 0.9695144 0.9695144 0.970 NA NA
DNMT3A belinostat 0.9723117 0.9723117 0.972 NA NA
CYP19A1 vismodegib 0.9723531 0.9773105 0.97 NA NA
BRCA2 vismodegib 0.9733078 0.9773105 0.97 NA NA
PML lapatinib 0.9738117 0.9878187 0.974 NA NA
FLT3 vismodegib 0.9773105 0.9773105 0.98 NA NA
MYD88 dabrafenib 0.9773916 0.9949724 0.97739 NA NA
PDGFRB trametinib 0.9784811 0.9784811 0.97848 NA NA
KRAS alectinib 0.9838427 0.9851623 0.984 NA NA
TPMT bosutinib monohydrate 0.9847665 0.9984574 0.985 NA NA
ESR1 alectinib 0.9851623 0.9851623 0.985 NA NA
PTCH1 lapatinib 0.9878187 0.9878187 0.988 NA NA
PTCH1 imatinib 0.9882665 1.0000000 0.988 NA NA
JAK2 ponatinib 0.9882767 0.9972326 0.988 NA NA
ALK nilotinib 0.9911966 0.9911966 0.991 NA NA
MGMT ruxolitinib 0.9946721 0.9946721 0.995 NA NA
ROS1 dabrafenib 0.9949724 0.9949724 0.99497 NA NA
ABL1 tretinoin 0.9968006 0.9968006 0.9968 NA NA
AKT1 ponatinib 0.9972326 0.9972326 0.997 NA NA
JAK2 bosutinib monohydrate 0.9984574 0.9984574 0.998 NA NA
DNMT3A pazopanib 0.9995159 0.9995159 0.9995 NA NA
MGMT imatinib 1.0000000 1.0000000 1.000 NA NA

6 Boxplots

Mutation status boxplots for genes identified in G2P.

6.1 Figure 1

Boxplots ordered by gene based on frequency of G2P associations.

6.1.1 CRISPR

crispr_data_g2p$Hugo_Symbol <- factor(crispr_data_g2p$Hugo_Symbol, levels = g2p_genes)
crispr_data_g2p_color <- as.character(crispr_data_g2p$Color_Nonsilent)
names(crispr_data_g2p_color) <- crispr_data_g2p$Mutation_Status_Nonsilent

crispr_label_text <- data.frame(p.signif.adj = crispr_signif_g2p_gene$p.signif.adj, p.signif = crispr_signif_g2p_gene$p.signif, Hugo_Symbol = crispr_signif_g2p_gene$Hugo_Symbol)
crispr_label_text <- filter(crispr_label_text, Hugo_Symbol %in% g2p_genes)
crispr_label_text$Hugo_Symbol <- factor(crispr_label_text$Hugo_Symbol, levels = g2p_genes)

crispr_data_g2p_plot <- ggplot(data = crispr_data_g2p, aes(x = Mutation_Status_Nonsilent, y = Score)) +
  facet_wrap(~ Hugo_Symbol, drop = FALSE, nrow = 1) +
  geom_hline(yintercept = 0, lty = 2, color = "darkgray") +
  geom_boxplot(mapping = aes(fill = Mutation_Status_Nonsilent), position = position_dodge(0.85)) +
  scale_fill_manual(values = crispr_data_g2p_color) +
  guides(color = FALSE) +
  geom_text(data = crispr_label_text, mapping = aes(x = 1.5, y = 2, label = p.signif), nudge_y = 0.1) +
  theme(legend.position = "top", axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank()) +
  labs(title = "CRISPR", fill = "Mutation Status", y = "CERES Score", x = "Mutation Status")
crispr_data_g2p_plot

# ggsave("./plots_18Q3/manuscript/crispr_data_g2p_plot.png", crispr_data_g2p_plot, device = "png", dpi = 450, width = 20, height = 4, units = "in")

6.1.2 CCLE

Plot code:

ccle_drugs <- as.character(unique(ccle_data_g2p_genesfilt$Drug))
ccle_g2p_drugboxplots <- lapply(ccle_drugs, makeDrugBoxplots, dataset = "CCLE")
ccle_g2p_drugboxplots_paths <- paste0(ccle_drugs, "_ccle_drugboxplots.png")
pwalk(list(ccle_g2p_drugboxplots_paths, ccle_g2p_drugboxplots), ggsave, path = "./plots_18Q3/manuscript/ccle_g2p_drugboxplots", dpi = 450, width = 20, height = 4, units = "in")

Data management:

ccle_g2p_drugboxplots <- paste0(list.files("./plots_18Q3/manuscript/ccle_g2p_drugboxplots", full.names = TRUE))
names(ccle_g2p_drugboxplots) <- str_replace_all(ccle_g2p_drugboxplots, c("_ccle_drugboxplots.png" = "", "./plots_18Q3/manuscript/ccle_g2p_drugboxplots/" = ""))
bsselect(ccle_g2p_drugboxplots, type = "img", live_search = TRUE, show_tick = TRUE, height = 300, frame_height = 275)

6.1.3 CTRP

Plot code:

ctrp_drugs <- as.character(unique(ctrp_data_g2p_genesfilt$Drug))
ctrp_g2p_drugboxplots <- lapply(ctrp_drugs, makeDrugBoxplots, dataset = "ctrp")
ctrp_g2p_drugboxplots_paths <- paste0(ctrp_drugs, "_ctrp_drugboxplots.png")
pwalk(list(ctrp_g2p_drugboxplots_paths, ctrp_g2p_drugboxplots), ggsave, path = "./plots_18Q3/manuscript/ctrp_g2p_drugboxplots", dpi = 450, width = 20, height = 4, units = "in")

Data management:

ctrp_g2p_drugboxplots <- paste0(list.files("./plots_18Q3/manuscript/ctrp_g2p_drugboxplots", full.names = TRUE))
names(ctrp_g2p_drugboxplots) <- str_replace_all(ctrp_g2p_drugboxplots, c("_ctrp_drugboxplots.png" = "", "./plots_18Q3/manuscript/ctrp_g2p_drugboxplots/" = ""))
bsselect(ctrp_g2p_drugboxplots, type = "img", live_search = TRUE, show_tick = TRUE, height = 300, frame_height = 275)

6.1.4 GDSC

Plot code:

gdsc_drugs <- as.character(unique(gdsc_data_g2p_genesfilt$Drug))
gdsc_g2p_drugboxplots <- lapply(gdsc_drugs, makeDrugBoxplots, dataset = "gdsc")
gdsc_g2p_drugboxplots_paths <- paste0(gdsc_drugs, "_gdsc_drugboxplots.png")
pwalk(list(gdsc_g2p_drugboxplots_paths, gdsc_g2p_drugboxplots), ggsave, path = "./plots_18Q3/manuscript/gdsc_g2p_drugboxplots", dpi = 450, width = 20, height = 4, units = "in")

Data management:

gdsc_g2p_drugboxplots <- paste0(list.files("./plots_18Q3/manuscript/gdsc_g2p_drugboxplots", full.names = TRUE))
names(gdsc_g2p_drugboxplots) <- str_replace_all(gdsc_g2p_drugboxplots, c("_gdsc_drugboxplots.png" = "", "./plots_18Q3/manuscript/gdsc_g2p_drugboxplots/" = ""))
bsselect(gdsc_g2p_drugboxplots, type = "img", live_search = TRUE, show_tick = TRUE, height = 300, frame_height = 275)

6.2 Gene-drug associations only

6.2.1 CCLE

ccle_g2p_order <- as.character(ccle_signif_g2p$Drug_Gene)
ccle_data_g2p$Drug_Gene_Ordered <- factor(ccle_data_g2p$Drug_Gene, levels = ccle_g2p_order, labels = sapply(strsplit(x = ccle_g2p_order, split = "_"), function(x) paste0(x[1], "\n", x[2])))
ccle_signif_g2p$Drug_Gene_Ordered <- factor(ccle_signif_g2p$Drug_Gene, levels = ccle_g2p_order, labels = sapply(strsplit(x = ccle_g2p_order, split = "_"), function(x) paste0(x[1], "\n", x[2])))
ccle_data_g2p_color <- as.character(ccle_data_g2p$Color_Nonsilent)
names(ccle_data_g2p_color) <- ccle_data_g2p$Mutation_Status_Nonsilent

ccle_label_text <- data.frame(p.signif.adj = ccle_signif_g2p$p.signif.adj, p.signif = ccle_signif_g2p$p.signif, Drug_Gene_Ordered = ccle_signif_g2p$Drug_Gene_Ordered)

ccle_data_g2p_plot <- ggplot(data = ccle_data_g2p, aes(x = Mutation_Status_Nonsilent, y = AUC)) +
  facet_wrap(~ Drug_Gene_Ordered, drop = FALSE, nrow = 1) +
  geom_hline(yintercept = 0, lty = 2, color = "darkgray") +
  geom_boxplot(mapping = aes(fill = Mutation_Status_Nonsilent), position = position_dodge(0.85), outlier.shape = 3, outlier.size = 0.5) +
  scale_fill_manual(values = ccle_data_g2p_color) +
  guides(color = FALSE) +
  geom_text(data = ccle_label_text, mapping = aes(x = 1.5, y = 7, label = p.signif), nudge_y = 0.1) +
  theme(legend.position = "top", axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank()) +
  labs(title = "CCLE", fill = "Mutation Status", y = "AUC", x = "Mutation Status")
ccle_data_g2p_plot

# ggsave("./plots_18Q3/manuscript/ccle_data_g2p_plot.png", ccle_data_g2p_plot, device = "png", dpi = 450, width = 12, height = 4, units = "in")

6.2.2 CTRP

ctrp_g2p_order <- as.character(ctrp_signif_g2p$Drug_Gene)
ctrp_data_g2p$Drug_Gene_Ordered <- factor(ctrp_data_g2p$Drug_Gene, levels = ctrp_g2p_order, labels = sapply(strsplit(x = ctrp_g2p_order, split = "_"), function(x) paste0(x[1], "\n", x[2])))
ctrp_signif_g2p$Drug_Gene_Ordered <- factor(ctrp_signif_g2p$Drug_Gene, levels = ctrp_g2p_order, labels = sapply(strsplit(x = ctrp_g2p_order, split = "_"), function(x) paste0(x[1], "\n", x[2])))
ctrp_data_g2p_color <- as.character(ctrp_data_g2p$Color_Nonsilent)
names(ctrp_data_g2p_color) <- ctrp_data_g2p$Mutation_Status_Nonsilent

ctrp_label_text <- data.frame(p.signif.adj = ctrp_signif_g2p$p.signif.adj, p.signif = ctrp_signif_g2p$p.signif, Drug_Gene_Ordered = ctrp_signif_g2p$Drug_Gene_Ordered)

ctrp_data_g2p_plot <- ggplot(data = ctrp_data_g2p, aes(x = Mutation_Status_Nonsilent, y = AUC)) +
  facet_wrap(~ Drug_Gene_Ordered, drop = FALSE, nrow = 3) +
  geom_hline(yintercept = 0, lty = 2, color = "darkgray") +
  geom_boxplot(mapping = aes(fill = Mutation_Status_Nonsilent), position = position_dodge(0.85), outlier.shape = 3, outlier.size = 0.5) +
  scale_fill_manual(values = ctrp_data_g2p_color) +
  guides(color = FALSE) +
  geom_text(data = ctrp_label_text, mapping = aes(x = 1.5, y = 22.5, label = p.signif), nudge_y = 0.1) +
  theme(legend.position = "top", axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank()) +
  labs(title = "CTRP", fill = "Mutation Status", y = "AUC", x = "Mutation Status")
ctrp_data_g2p_plot

# ggsave("./plots_18Q3/manuscript/ctrp_data_g2p_plot.png", ctrp_data_g2p_plot, device = "png", dpi = 450, width = 12, height = 7, units = "in")

6.2.3 GDSC

gdsc_g2p_order <- as.character(gdsc_signif_g2p$Drug_Gene)
gdsc_data_g2p$Drug_Gene_Ordered <- factor(gdsc_data_g2p$Drug_Gene, levels = gdsc_g2p_order, labels = sapply(strsplit(x = gdsc_g2p_order, split = "_"), function(x) paste0(x[1], "\n", x[2])))
gdsc_signif_g2p$Drug_Gene_Ordered <- factor(gdsc_signif_g2p$Drug_Gene, levels = gdsc_g2p_order, labels = sapply(strsplit(x = gdsc_g2p_order, split = "_"), function(x) paste0(x[1], "\n", x[2])))
gdsc_data_g2p_color <- as.character(gdsc_data_g2p$Color_Nonsilent)
names(gdsc_data_g2p_color) <- gdsc_data_g2p$Mutation_Status_Nonsilent

gdsc_label_text <- data.frame(p.signif.adj = gdsc_signif_g2p$p.signif.adj, p.signif = gdsc_signif_g2p$p.signif, Drug_Gene_Ordered = gdsc_signif_g2p$Drug_Gene_Ordered)

gdsc_data_g2p_plot <- ggplot(data = gdsc_data_g2p, aes(x = Mutation_Status_Nonsilent, y = AUC)) +
  facet_wrap(~ Drug_Gene_Ordered, drop = FALSE, nrow = 3) +
  geom_hline(yintercept = 0, lty = 2, color = "darkgray") +
  geom_boxplot(mapping = aes(fill = Mutation_Status_Nonsilent), position = position_dodge(0.85), outlier.shape = 3, outlier.size = 0.5) +
  scale_fill_manual(values = gdsc_data_g2p_color) +
  guides(color = FALSE) +
  geom_text(data = gdsc_label_text, mapping = aes(x = 1.5, y = 1, label = p.signif), nudge_y = 0.1) +
  theme(legend.position = "top", axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank()) +
  labs(title = "GDSC", fill = "Mutation Status", y = "AUC", x = "Mutation Status")
gdsc_data_g2p_plot

# ggsave("./plots_18Q3/manuscript/gdsc_data_g2p_plot.png", gdsc_data_g2p_plot, device = "png", dpi = 450, width = 12, height = 7, units = "in")

7 CRISPR: Point mutation mapping

Match specific point mutations.

g2p_indications <- filter(read.delim("./data_munging/data_mutation_associations_appended.csv", sep = "\t", header = TRUE), Evidence.Level == "A")
maf_g2p_indications <- filter(maf_raw, Genome_Change %in% g2p_indications$MutationName)
crispr_g2p_indications <- merge(crispr_data_ptmuts, maf_g2p_indications, by = c("Hugo_Symbol", "CCLE_Name", "Broad_ID"), all.x = TRUE)
crispr_g2p_indications$Mutation_Status_Nonsilent <- ifelse(is.na(crispr_g2p_indications$Mutation_Status_Nonsilent), "Other", crispr_g2p_indications$Mutation_Status_Nonsilent)
dup_g2p_indications <- filter(crispr_g2p_indications[, c("Hugo_Symbol", "CCLE_Name", "Broad_ID")] %>% group_by(Hugo_Symbol, CCLE_Name, Broad_ID) %>% tally(), n > 1)
crispr_g2p_indications <- merge(crispr_g2p_indications, dup_g2p_indications, by = c("Hugo_Symbol", "CCLE_Name", "Broad_ID"), all.x = TRUE)
crispr_g2p_indications$Genome_Change <- factor(crispr_g2p_indications$Genome_Change, levels = unique(crispr_g2p_indications$Genome_Change))
crispr_g2p_indications_nonNA <- filter(crispr_g2p_indications, is.na(n))

crispr_g2p_indications_plot <- ggplot(crispr_g2p_indications, aes(x = Hugo_Symbol, y = Score)) +
  geom_hline(yintercept = 0, lty = 2, color = "darkgray") +
  geom_boxplot(alpha = 0.5, color = "lightgray", outlier.shape = NA) +
  geom_jitter(width = 0.3, mapping = aes(color = Mutation_Status_Nonsilent), size = 1) +
  theme_light() +
  theme(legend.title = element_blank()) +
  labs(x = "Gene", y = "CERES Score")
crispr_g2p_indications_plot

crispr_g2p_indications_muts_plot <- ggplot(crispr_g2p_indications, aes(x = Protein_Change, y = Score)) +
  facet_grid(~ Hugo_Symbol, scales = "free_x", space = "free") +
  geom_hline(yintercept = 0, lty = 2, color = "darkgray") +
  geom_boxplot(alpha = 0.5, color = "lightgray", outlier.shape = NA) +
  geom_jitter(width = 0.1, mapping = aes(color = Mutation_Status_Nonsilent), size = 1) +
  theme_light() +
  theme(legend.title = element_blank(), axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x = "Gene", y = "CERES Score")
crispr_g2p_indications_muts_plot

# ggsave("./plots_18Q3/manuscript/crispr_g2p_indications_plot_test.png", crispr_g2p_indications_plot, width = 12, height = 5, units = "in")
# ggsave("./plots_18Q3/manuscript/crispr_g2p_indications_muts_plot_test.png", crispr_g2p_indications_muts_plot, width = 20, height = 5, units = "in")

8 References

Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., … Garraway, L. A. (2012). The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 483(7391), 603–607. https://doi.org/10.1038/nature11003

Broad Institute Cancer Dependency Map; Cancer Data Science (2018): Cancer Dependency Map, CRISPR Avana dataset 18Q3 (Avana_public_18Q3). figshare. Fileset. doi:10.6084/m9.figshare.6931364.v1

Consortium, T. C. C. L. E., & Consortium, T. G. of D. S. in C. (2015). Pharmacogenomic agreement between two cancer cell line data sets. Nature, 528(7580), 84–87. https://doi.org/10.1038/nature15736

Data Science, Cancer (2018): DEMETER2 data. figshare. Fileset. doi:10.6084/m9.figshare.6025238.v2

Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., … Root, D. E. (2016). Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology, 34(2), 184–191. https://doi.org/10.1038/nbt.3437

Meyers, R. M., Bryan, J. G., McFarland, J. M., Weir, B. A., Sizemore, A. E., Xu, H., … Tsherniak, A. (2017). Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nature Genetics, 49(12), 1779–1784. https://doi.org/10.1038/ng.3984

McFarland, J. M., Ho, Z. V., Kugener, G., Dempster, J. M., Montgomery, P. G., Bryan, J. G., … Tsherniak, A. (2018). Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration. https://doi.org/10.1101/305656

9 Session information

print(sessionInfo())
## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS  10.14
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] bindrcpp_0.2.2    forcats_0.3.0     stringr_1.3.1    
##  [4] dplyr_0.7.6       purrr_0.2.5       readr_1.1.1      
##  [7] tidyr_0.8.1       tibble_1.4.2      tidyverse_1.2.1  
## [10] kableExtra_0.9.0  ggsignif_0.4.0    ggpubr_0.1.7.999 
## [13] magrittr_1.5      ggplot2_3.0.0     data.table_1.11.4
## [16] bsselectR_0.1.0  
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_0.2.4  haven_1.1.2       lattice_0.20-35  
##  [4] colorspace_1.3-2  htmltools_0.3.6   viridisLite_0.3.0
##  [7] yaml_2.1.19       rlang_0.2.1       pillar_1.3.0     
## [10] glue_1.3.0        withr_2.1.2       modelr_0.1.2     
## [13] readxl_1.1.0      bindr_0.1.1       plyr_1.8.4       
## [16] cellranger_1.1.0  munsell_0.5.0     gtable_0.2.0     
## [19] rvest_0.3.2       htmlwidgets_1.2   evaluate_0.11    
## [22] labeling_0.3      knitr_1.20        parallel_3.5.0   
## [25] highr_0.7         broom_0.5.0       Rcpp_0.12.18     
## [28] scales_1.0.0      backports_1.1.2   jsonlite_1.5.9000
## [31] hms_0.4.2         digest_0.6.15     stringi_1.2.4    
## [34] grid_3.5.0        rprojroot_1.3-2   cli_1.0.0        
## [37] tools_3.5.0       lazyeval_0.2.1    crayon_1.3.4     
## [40] pkgconfig_2.0.1   xml2_1.2.0        lubridate_1.7.4  
## [43] assertthat_0.2.0  rmarkdown_1.10    httr_1.3.1       
## [46] rstudioapi_0.7    R6_2.2.2          nlme_3.1-137     
## [49] compiler_3.5.0